Biomarkers for the detection of early stage ovarian cancer

ABSTRACT

The present invention provides methods and compositions for detecting early stage ovarian cancer in a patient. Also, methods for evaluating the ovarian cancer state of a patient are described herein. These methods involve the detection, analysis, and classification of biomarkers in biological samples.

CROSS-REFERENCE TO RELATED APPLICATIONS

The application is a continuation of PCT/US2008/012323 filed Oct. 29,2008, which claims the benefit of U.S. Provisional Patent Application60/983,378 filed on Oct. 29, 2007. The entire contents of each of theaforementioned applications is hereby incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to a method of determining if a subjecthas ovarian cancer comprising: (a) measuring biomarkers in a sample fromthe subject and (b) correlating the measurement with the presence ofovarian cancer. Specifically, the biomarkers disclosed herein areeffective for determining if a subject has early stage ovarian cancer.The invention further relates to kits for determining the diagnosis of asubject.

BACKGROUND OF THE INVENTION

Ovarian cancer is among the most lethal gynecologic malignancies indeveloped countries. Annually in the United States alone, approximately23,000 women are diagnosed with the disease and almost 14,000 women diefrom it. (Jamal, A., et al., CA Cancer J. Clin, 2002; 52:23-47). Despiteprogress in cancer therapy, ovarian cancer mortality has remainedvirtually unchanged over the past two decades. (Id.) Given the steepsurvival gradient relative to the stage at which the disease isdiagnosed, early detection remains the most important factor inimproving long-term survival of ovarian cancer patients.

The poor prognosis of ovarian cancer diagnosed at late stages, the costand risk associated with confirmatory diagnostic procedures, and itsrelatively low prevalence in the general population together poseextremely stringent requirements on the sensitivity and specificity of atest for it to be used for screening for ovarian cancer in the generalpopulation.

The identification of tumor markers suitable for the early detection anddiagnosis of cancer holds great promise to improve the clinical outcomeof patients. It is especially important for patients presenting withvague or no symptoms or with tumors that are relatively inaccessible tophysical examination. Despite considerable effort directed at earlydetection, no cost effective screening tests have been developed (PaleyP J., Curr Opin Oncol, 2001; 13(5):399-402) and women generally presentwith disseminated disease at diagnosis. (Ozols R F, et al., Epithelialovarian cancer. In: Hoskins W J, Perez Calif., Young R C, editors.Principles and Practice of Gynecologic Oncology. 3rd ed. Philadelphia:Lippincott, Williams and Wilkins; 2000. p. 981-1057).

The best-characterized tumor marker, CA125, is negative in approximately30-40% of stage I ovarian carcinomas and its levels are elevated in avariety of benign diseases. (Meyer T, et al., Br J Cancer, 2000;82(9):1535-8; Buamah P., J Surg Oncol, 2000; 75(4):264-5; Tuxen M K, etal., Cancer Treat Rev, 1995; 21(3):215-45). Its use as apopulation-based screening tool for early detection and diagnosis ofovarian cancer is hindered by its low sensitivity and specificity.(MacDonald N D, et al., Eur J Obstet Gynecol Reprod Biol, 1999;82(2):155-7; Jacobs I, et al., Hum Reprod, 1989; 4(1):1-12; Shih I-M, etal., Tumor markers in ovarian cancer. In: Diamandis E P, Fritsche, H.,Lilja, H., Chan, D. W., and Schwartz, M., editor. Tumor markersphysiology, pathobiology, technology and clinical applications.Philadelphia: AACC Press; in press). Although pelvic and more recentlyvaginal sonography has been used to screen high-risk patients, neithertechnique has the sufficient sensitivity and specificity to be appliedto the general population. (MacDonald N D, et al., supra). Recentefforts in using CA125 in combination with additional tumor markers(Woolas R P X F, et al., J Natl Cancer Inst, 1993; 85(21):1748-51;Woolas R P, et al., Gynecol Oncol, 1995; 59(1):111-6; Zhang Z, et al.,Gynecol Oncol, 1999; 73(1):56-61; Zhang Z, et al., Use of MultipleMarkers to Detect Stage I Epithelial Ovarian Cancers: Neural NetworkAnalysis Improves Performance. American Society of Clinical Oncology2001; Annual Meeting, Abstract) in a longitudinal risk of cancer model(Skates S J, et al., Cancer, 1995; 76 (10 Suppl):2004-10), and in tandemwith ultrasound as a second line test (Jacobs I D A, et al., Br Med J,1993; 306(6884):1030-34; Menon U T A, et al., British Journal ofObstetrics and Gynecology, 2000; 107(2):165-69) have shown promisingresults in improving overall test specificity, which is critical for adisease such as ovarian cancer that has a relatively low prevalence.

Due to the dismal prognosis of late stage ovarian cancer, it is thegeneral consensus that a physician will accept a test with a minimalpositive predictive value of 10%. (Bast, R. C., et al., Cancer Treatmentand Research, 2002; 107:61-97). Extending this to the generalpopulation, a general screening test would require a sensitivity greaterthan 70% and a specificity of 99.6%. Currently, none of the existingserologic markers, such as CA125, CA72-4, or M-CSF, individuallydelivers such a performance. (Bast, R. C., et al., Int J Biol Markers,1998; 13:179-87).

Thus, there is a critical need to identify one or more panels ofbiomarkers that deliver the required sensitivity and specificity forearly detection of ovarian cancer. Without an acceptable screening test,early detection remains the most critical factor in improving long-termsurvival of patients with ovarian cancer.

Thus, it is desirable to have a reliable and accurate method ofdetermining the ovarian cancer status in patients, the results of whichcan then be used to manage subject treatment.

SUMMARY

The present invention provides sensitive and quick methods and kits thatare useful for determining the ovarian cancer status of patients orsubjects by measuring and identifying particular biomarkers. Thedetection and measurement of these biomarkers in patient samplesprovides information that diagnosticians can correlate with the presenceor absence of ovarian cancer. The markers are characterized bymass/charge ratio, molecular weight and/or by their known proteinidentities. The markers can be resolved from other proteins in a sampleby using a variety of fractionation techniques, e.g., chromatographicseparation coupled with mass spectrometry, protein capture usingimmobilized antibodies, bead-protein complexes or by traditionalimmunoassays. In preferred embodiments, the method of resolutioninvolves Surface-Enhanced Laser Desorption/Ionization (“SELDI”) massspectrometry, in which the surface of the mass spectrometry probecomprises adsorbents that bind the markers.

More specifically, two panels of markers were identified herein. Thefirst panel comprises (i) apolipoprotein A1 (ApoA1), (ii) transthyretin(TTR), (iii) inter-alpha (globulin) inhibitor H4 (plasmaKallikrein-sensitive glycoprotein) (ITIH4), (iv) transferrin (TFR) and(v) CA125. The second panel comprises (i) apolipoprotein A1 (ApoA1),(ii) transthyretin (TTR), (iii) CTAPIII, and (iv) CA125. These panels ormarkers are detecting early stage ovarian cancer, e.g., Stage 1 or StageII.

The present invention provides a method of assessing the ovarian cancerstatus in a subject comprising (a) measuring the panel of biomarkers ina sample from the subject, (b) and correlating the measurement withovarian cancer status. In certain methods, the measuring step comprisesdetecting the m/z (mass-to-charge ratio) values of markers in thesample.

Preferred methods of the invention also include assessing ovarian cancerpatient status comprising:

(a) determining the concentration or expression levels or peak intensityvalues of the biomarkers in a sample from the subject, wherein thebiomarkers are: (i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR),(iii) inter-alpha (globulin) inhibitor H4 (plasma Kallikrein-sensitiveglycoprotein) (ITIH4), (iv) transferrin (TFR) and (v) CA125; or (i)apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii) CTAPIII, and(iv) CA125;

(b) correlating the corresponding concentration/expression levels/peakintensity values with ovarian cancer status.

The invention also relates to methods wherein the measuring stepcomprises: providing a subject sample of blood or a blood derivative;fractionating proteins in the sample on an anion exchange resin andcollecting fractions that contain (i) apolipoprotein A1 (ApoA1), (ii)transthyretin (TTR), (iii) inter-alpha (globulin) inhibitor H4 (plasmaKallikrein-sensitive glycoprotein) (ITIH4), (iv) transferrin (TFR) and(v) CA125; or (i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR),(iii) CTAPIII, and (iv) CA125 from the fractions on a surface of asubstrate comprising capture reagents that bind the protein biomarkers.The blood derivative is, e.g., serum or plasma. In preferredembodiments, the substrate is a SELDI probe comprising an IMAC coppersurface and wherein the protein biomarkers are detected by SELDI. Inother embodiments, the substrate is a SELDI probe comprising biospecificaffinity reagents that bind one or more of the thirteen biomarkers andwherein the protein biomarkers are detected by SELDI. In otherembodiments, the substrate is a microtiter plate comprising biospecificaffinity reagents that bind (i) apolipoprotein A1 (ApoA1), (ii)transthyretin (TTR), (iii) inter-alpha (globulin) inhibitor H4 (plasmaKallikrein-sensitive glycoprotein) (ITIH4), (iv) transferrin (TFR) and(v) CA125; or (i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR),(iii) CTAPIII, and (iv) CA125; and the protein biomarkers are detectedby immunoassay.

In certain embodiments, the methods further comprise managing subjecttreatment based on the status determined by the method. For example, ifthe result of the methods of the present invention is inconclusive orthere is reason that confirmation of status is necessary, the physicianmay order more tests. Alternatively, if the status indicates thatsurgery is appropriate, the physician may schedule the patient forsurgery. Furthermore, if the results show that treatment has beensuccessful, no further management may be necessary.

The invention also provides for such methods where the panel ofbiomarkers is measured again after subject management. In theseinstances, the step of managing subject treatment is then repeatedand/or altered depending on the result obtained.

The term “ovarian cancer status” refers to the status of the patient.Examples of types of ovarian cancer statuses include, but are notlimited to, disease free or having ovarian cancer. More specifically,the status may be having early stage ovarian cancer, e.g., stage I orstage II. Another type of status is “treatment responsiveness” i.e.whether a patient has a high or low likelihood of responding to a giventype of therapy. A third type of status is “remission” i.e. whether apatient is deemed to be free of disease (in remission) or to have cancerafter one more therapeutic interventions (in recurrence). Other statusesand degrees of each status are known in the art.

The biomarkers that are useful in the methods of the present inventionare (i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii)inter-alpha (globulin) inhibitor H4 (plasma Kallikrein-sensitiveglycoprotein) (ITIH4), (iv) transferrin (TFR) and (v) CA125; or (i)apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii) CTAPIII, and(iv) CA125. The age of the patient is another biomarker of the presentinvention, wherein the older the patient, the poorer the prognosis forthe patient.

For the mass values of the markers disclosed herein, the mass accuracyof the spectral instrument is considered to be about within +/−0.15percent of the disclosed molecular weight value. Additionally, to suchrecognized accuracy variations of the instrument, the spectral massdetermination can vary within resolution limits of from about 400 to1000 m/dm, where m is mass and dm is the mass spectral peak width at 0.5peak height. Those mass accuracy and resolution variances associatedwith the mass spectral instrument and operation thereof are reflected inthe use of the term “about” in the disclosure of the mass of each ofseven biomarkers. It is also intended that such mass accuracy andresolution variances and thus meaning of the term “about” with respectto the mass of each of the markers disclosed herein is inclusive ofvariants of the markers as may exist due to sex, genotype and/orethnicity of the subject and the particular cancer or origin or stagethereof.

The accuracy of a diagnostic test is characterized by a ReceiverOperating Characteristic curve (“ROC curve”). An ROC is a plot of thetrue positive rate against the false positive rate for the differentpossible cutpoints of a diagnostic test. An ROC curve shows therelationship between sensitivity and specificity. That is, an increasein sensitivity will be accompanied by a decrease in specificity. Thecloser the curve follows the left axis and then the top edge of the ROCspace, the more accurate the test. Conversely, the closer the curvecomes to the 45-degree diagonal of the ROC graph, the less accurate thetest. The area under the ROC is a measure of test accuracy. The accuracyof the test depends on how well the test separates the group beingtested into those with and without the disease in question. An areaunder the curve (referred to as “AUC”) of 1 represents a perfect test,while an area of 0.5 represents a test of no use. Thus, preferredbiomarkers and diagnostic methods of the present invention have an AUCgreater than 0.50, more preferred tests have an AUC greater than 0.60,more preferred tests have an AUC greater than 0.70.

Preferred methods of measuring the biomarkers include use of a biochiparray. Biochip arrays useful in the invention include protein andnucleic acid arrays. One or more markers are captured on the biochiparray and subjected to laser ionization to detect the molecular weightof the markers. Analysis of the markers is, for example, by molecularweight of the one or more markers against a threshold intensity that isnormalized against total ion current. Preferably, logarithmictransformation is used for reducing peak intensity ranges to limit thenumber of markers detected.

Another preferred method of measuring the biomarkers includes the use ofa combinatorial ligand library synthesized on beads as described in U.S.Ser. No. 11/495,842, filed Jul. 28, 2006 and entitled “Methods forReducing the range in Concentrations of Analyte Species in a Sample”;hereby incorporated by reference in its entirety.

In preferred methods of the present invention, the step of correlatingthe measurement of the biomarkers with ovarian cancer status isperformed by a software classification algorithm. Preferably, data isgenerated on immobilized subject samples on a biochip array, bysubjecting said biochip array to laser ionization and detectingintensity of signal for mass/charge ratio; and, transforming the datainto computer readable form; and executing an algorithm that classifiesthe data according to user input parameters, for detecting signals thatrepresent markers present in ovarian cancer patients and are lacking innon-cancer subject controls.

Preferably the biochip surfaces are, for example, ionic, anionic,comprised of immobilized nickel ions, comprised of a mixture of positiveand negative ions, comprised of one or more antibodies, single or doublestranded nucleic acids, proteins, peptides or fragments thereof, aminoacid probes, or phage display libraries.

In other preferred methods one or more of the markers are measured usinglaser desorption/ionization mass spectrometry, comprising providing aprobe adapted for use with a mass spectrometer comprising an adsorbentattached thereto, and contacting the subject sample with the adsorbent,and; desorbing and ionizing the marker or markers from the probe anddetecting the deionized/ionized markers with the mass spectrometer.

Preferably, the laser desorption/ionization mass spectrometry comprises:providing a substrate comprising an adsorbent attached thereto;contacting the subject sample with the adsorbent; placing the substrateon a probe adapted for use with a mass spectrometer comprising anadsorbent attached thereto; and, desorbing and ionizing the marker ormarkers from the probe and detecting the desorbed/ionized marker ormarkers with the mass spectrometer.

The adsorbent can for example be hydrophobic, hydrophilic, ionic ormetal chelate adsorbent, such as, nickel or an antibody, single- ordouble stranded oligonucleotide, amino acid, protein, peptide orfragments thereof.

The methods of the present invention can be performed on any type ofpatient sample that would be amenable to such methods, e.g., blood,serum and plasma.

The present invention also provides kits comprising (a) capture reagentsthat bind a biomarkers comprising (i) apolipoprotein A1 (ApoA1), (ii)transthyretin (TTR), (iii) inter-alpha (globulin) inhibitor H4 (plasmaKallikrein-sensitive glycoprotein) (ITIH4), (iv) transferrin (TFR) and(v) CA125; or (i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR),(iii) CTAPIII, and (iv) CA125; and (b) a container comprising the panelof biomarkers. While the capture reagent can be any type of reagent,preferably the reagent is a SELDI probe.

In certain kits of the present invention, the capture reagent comprisesan immobilized metal chelate (“IMAC”).

Certain kits of the present invention further comprise a wash solutionthat selectively allows retention of the bound biomarker to the capturereagent as compared with other biomarkers after washing.

The invention also provides kits comprising (a) capture reagents thatbinds at least one biomarkers comprising (i) apolipoprotein A1 (ApoA1),(ii) transthyretin (TTR), (iii) inter-alpha (globulin) inhibitor H4(plasma Kallikrein-sensitive glycoprotein) (ITIH4), (iv) transferrin(TFR) and (v) CA125; or (i) apolipoprotein A1 (ApoA1), (ii)transthyretin (TTR), (iii) CTAPIII, and (iv) CA125; and (b) instructionsfor using the capture reagent to measure the biomarker. In certain ofthese kits, the capture reagent comprises an antibody. Furthermore, somekits further comprise an MS probe to which the capture reagent isattached or is attachable. In some kits, the capture reagent comprisesan IMAC. The kits may also contain a wash solution that selectivelyallows retention of the bound biomarker to the capture reagent ascompared with other biomarkers after washing. Preferably, the kitcomprises written instructions for use of the kit for determiningovarian cancer status and the instructions provide for contacting a testsample with the capture reagents and measuring one or more biomarkersretained by the capture reagents.

The kit also provides for capture reagents, which are antibodies, singleor double stranded oligonucleotide, amino acid, protein, peptide orfragments thereof.

Measurement of one or more protein biomarkers using the kit, is by massspectrometry or immunoassays such as an ELISA.

Purified proteins for detection of ovarian cancer and/or generation ofantibodies for further diagnostic assays are also provided for. Purifiedproteins include purified peptides of apolipoprotein A1 (ApoA1),transthyretin (TTR), inter-alpha (globulin) inhibitor H4 (plasmaKallikrein-sensitive glycoprotein) (ITIH4), transferrin (TFR), CA125, orCTAPIII. The invention also provides these purified peptides furthercomprising a detectable label.

The invention also provides an article manufacture comprising capturereagents bound to the panel of biomarkers.

The present invention also provides a system comprising a plurality ofcapture reagents each of which has bound to it a different biomarkercomprising ((i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR),(iii) inter-alpha (globulin) inhibitor H4 (plasma Kallikrein-sensitiveglycoprotein) (ITIH4), (iv) transferrin (TFR) and (v) CA125; or (i)apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii) CTAPIII, and(iv) CA125.

Other aspects of the invention are described infra.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts the protocol workflow. Serum samples were run intriplicate in a randomized fashion in 96 well plates, using roboticautomation for sample preparation.

FIG. 2 depicts ROC curve plots for models trained on six subsets ofdata. The black dot represents CA125 performance alone. Performance ofthe models, however, was always assessed by comparing normal controls toearly stage (I or II) ovarian cancer. Panel A depicts the proteomicmarkers without CA125, and Panel B depicts the proteomic markers withCA125.

DEFINITIONS

Unless defined otherwise, all technical and scientific terms used hereinhave the meaning commonly understood by a person skilled in the art towhich this invention belongs. The following references provide one ofskill with a general definition of many of the terms used in thisinvention: Singleton et al., Dictionary of Microbiology and MolecularBiology (2nd ed. 1994); The Cambridge Dictionary of Science andTechnology (Walker ed., 1988); The Glossary of Genetics, 5th Ed., R.Rieger et al. (eds.), Springer Verlag (1991); and Hale & Marham, TheHarper Collins Dictionary of Biology (1991). As used herein, thefollowing terms have the meanings ascribed to them unless specifiedotherwise.

“Gas phase ion spectrometer” refers to an apparatus that detects gasphase ions. Gas phase ion spectrometers include an ion source thatsupplies gas phase ions. Gas phase ion spectrometers include, forexample, mass spectrometers, ion mobility spectrometers, and total ioncurrent measuring devices. “Gas phase ion spectrometry” refers to theuse of a gas phase ion spectrometer to detect gas phase ions.

“Mass spectrometer” refers to a gas phase ion spectrometer that measuresa parameter that can be translated into mass-to-charge ratios of gasphase ions. Mass spectrometers generally include an ion source and amass analyzer. Examples of mass spectrometers are time-of-flight,magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance,electrostatic sector analyzer and hybrids of these. “Mass spectrometry”refers to the use of a mass spectrometer to detect gas phase ions.

“Laser desorption mass spectrometer” refers to a mass spectrometer thatuses laser energy as a means to desorb, volatilize, and ionize ananalyte.

“Tandem mass spectrometer” refers to any mass spectrometer that iscapable of performing two successive stages of m/z-based discriminationor measurement of ions, including ions in an ion mixture. The phraseincludes mass spectrometers having two mass analyzers that are capableof performing two successive stages of m/z-based discrimination ormeasurement of ions tandem-in-space. The phrase further includes massspectrometers having a single mass analyzer that is capable ofperforming two successive stages of m/z-based discrimination ormeasurement of ions tandem-in-time. The phrase thus explicitly includesQq-TOF mass spectrometers, ion trap mass spectrometers, ion trap-TOFmass spectrometers, TOF-TOF mass spectrometers, Fourier transform ioncyclotron resonance mass spectrometers, electrostatic sector-magneticsector mass spectrometers, and combinations thereof.

“Mass analyzer” refers to a sub-assembly of a mass spectrometer thatcomprises means for measuring a parameter that can be translated intomass-to-charge ratios of gas phase ions. In a time-of-flight massspectrometer the mass analyzer comprises an ion optic assembly, a flighttube and an ion detector.

“Ion source” refers to a sub-assembly of a gas phase ion spectrometerthat provides gas phase ions. In one embodiment, the ion source providesions through a desorption/ionization process. Such embodiments generallycomprise a probe interface that positionally engages a probe in aninterrogatable relationship to a source of ionizing energy (e.g., alaser desorption/ionization source) and in concurrent communication atatmospheric or subatmospheric pressure with a detector of a gas phaseion spectrometer.

Forms of ionizing energy for desorbing/ionizing an analyte from a solidphase include, for example: (1) laser energy; (2) fast atoms (used infast atom bombardment); (3) high energy particles generated via betadecay of radionucleides (used in plasma desorption); and (4) primaryions generating secondary ions (used in secondary ion massspectrometry). The preferred form of ionizing energy for solid phaseanalytes is a laser (used in laser desorption/ionization), inparticular, nitrogen lasers, Nd-Yag lasers and other pulsed lasersources. “Fluence” refers to the energy delivered per unit area ofinterrogated image. A high fluence source, such as a laser, will deliverabout 1 mJ/mm2 to 50 mJ/mm2 Typically, a sample is placed on the surfaceof a probe, the probe is engaged with the probe interface and the probesurface is struck with the ionizing energy. The energy desorbs analytemolecules from the surface into the gas phase and ionizes them.

Other forms of ionizing energy for analytes include, for example: (1)electrons that ionize gas phase neutrals; (2) strong electric field toinduce ionization from gas phase, solid phase, or liquid phase neutrals;and (3) a source that applies a combination of ionization particles orelectric fields with neutral chemicals to induce chemical ionization ofsolid phase, gas phase, and liquid phase neutrals.

“Solid support” refers to a solid material which can be derivatizedwith, or otherwise attached to, a capture reagent. Exemplary solidsupports include probes, microtiter plates and chromatographic resins.

“Probe” in the context of this invention refers to a device adapted toengage a probe interface of a gas phase ion spectrometer (e.g., a massspectrometer) and to present an analyte to ionizing energy forionization and introduction into a gas phase ion spectrometer, such as amass spectrometer. A “probe” will generally comprise a solid substrate(either flexible or rigid) comprising a sample presenting surface onwhich an analyte is presented to the source of ionizing energy.

“Biomarker panel” refers to one of the two biomarker panels set forthherein. Specifically, the first biomarker panel comprises (i)apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii) inter-alpha(globulin) inhibitor H4 (plasma Kallikrein-sensitive glycoprotein)(ITIH4), (iv) transferrin (TFR) and (v) CA125. The second biomarkerpanel comprises (i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR),(iii) CTAPIII, and (iv) CA125.

“Surface-enhanced laser desorption/ionization” or “SELDI” refers to amethod of desorption/ionization gas phase ion spectrometry (e.g., massspectrometry) in which the analyte is captured on the surface of a SELDIprobe that engages the probe interface of the gas phase ionspectrometer. In “SELDI MS,” the gas phase ion spectrometer is a massspectrometer. SELDI technology is described in, e.g., U.S. Pat. No.5,719,060 (Hutchens and Yip) and U.S. Pat. No. 6,225,047 (Hutchens andYip).

“Surface-Enhanced Affinity Capture” or “SEAC” is a version of SELDI thatinvolves the use of probes comprising an absorbent surface (a “SEACprobe”). “Adsorbent surface” refers to a surface to which is bound anadsorbent (also called a “capture reagent” or an “affinity reagent”). Anadsorbent is any material capable of binding an analyte (e.g., a targetpolypeptide or nucleic acid). “Chromatographic adsorbent” refers to amaterial typically used in chromatography. Chromatographic adsorbentsinclude, for example, ion exchange materials, metal chelators (e.g.,nitriloacetic acid or iminodiacetic acid), immobilized metal chelates,hydrophobic interaction adsorbents, hydrophilic interaction adsorbents,dyes, simple biomolecules (e.g., nucleotides, amino acids, simple sugarsand fatty acids) and mixed mode adsorbents (e.g., hydrophobicattraction/electrostatic repulsion adsorbents). “Biospecific adsorbent”refers an adsorbent comprising a biomolecule, e.g., a nucleic acidmolecule (e.g., an aptamer), a polypeptide, a polysaccharide, a lipid, asteroid or a conjugate of these (e.g., a glycoprotein, a lipoprotein, aglycolipid, a nucleic acid (e.g., DNA)-protein conjugate). In certaininstances the biospecific adsorbent can be a macromolecular structuresuch as a multiprotein complex, a biological membrane or a virus.Examples of biospecific adsorbents are antibodies, receptor proteins andnucleic acids. Biospecific adsorbents typically have higher specificityfor a target analyte than chromatographic adsorbents. Further examplesof adsorbents for use in SELDI can be found in U.S. Pat. No. 6,225,047(Hutchens and Yip, “Use of retentate chromatography to generatedifference maps,” May 1, 2001).

In some embodiments, a SEAC probe is provided as a pre-activated surfacewhich can be modified to provide an adsorbent of choice. For example,certain probes are provided with a reactive moiety that is capable ofbinding a biological molecule through a covalent bond. Epoxide andcarbodiimidizole are useful reactive moieties to covalently bindbiospecific adsorbents such as antibodies or cellular receptors.

“Adsorption” refers to detectable non-covalent binding of an analyte toan adsorbent or capture reagent.

“Surface-Enhanced Neat Desorption” or “SEND” is a version of SELDI thatinvolves the use of probes comprising energy absorbing moleculeschemically bound to the probe surface. (“SEND probe.”) “Energy absorbingmolecules” (“EAM”) refer to molecules that are capable of absorbingenergy from a laser desorption/ionization source and thereaftercontributing to desorption and ionization of analyte molecules incontact therewith. The phrase includes molecules used in MALDI,frequently referred to as “matrix”, and explicitly includes cinnamicacid derivatives, sinapinic acid (“SPA”), cyano-hydroxy-cinnamic acid(“CHCA”) and dihydroxybenzoic acid, ferulic acid, hydroxyacetophenonederivatives, as well as others. It also includes EAMs used in SELDI.SEND is further described in U.S. Pat. No. 5,719,060 and U.S. patentapplication 60/408,255, filed Sep. 4, 2002 (Kitagawa, “Monomers AndPolymers Having Energy Absorbing Moieties Of Use InDesorption/Ionization Of Analytes”).

“Surface-Enhanced Photolabile Attachment and Release” or “SEPAR” is aversion of SELDI that involves the use of probes having moietiesattached to the surface that can covalently bind an analyte, and thenrelease the analyte through breaking a photolabile bond in the moietyafter exposure to light, e.g., laser light. SEPAR is further describedin U.S. Pat. No. 5,719,060.

“Eluant” or “wash solution” refers to an agent, typically a solution,which is used to affect or modify adsorption of an analyte to anadsorbent surface and/or remove unbound materials from the surface. Theelution characteristics of an eluant can depend, for example, on pH,ionic strength, hydrophobicity, degree of chaotropism, detergentstrength and temperature.

“Analyte” refers to any component of a sample that is desired to bedetected. The term can refer to a single component or a plurality ofcomponents in the sample.

The “complexity” of a sample adsorbed to an adsorption surface of anaffinity capture probe means the number of different protein speciesthat are adsorbed.

“Molecular binding partners” and “specific binding partners” refer topairs of molecules, typically pairs of biomolecules that exhibitspecific binding. Molecular binding partners include, withoutlimitation, receptor and ligand, antibody and antigen, biotin andavidin, and biotin and streptavidin.

“Monitoring” refers to recording changes in a continuously varyingparameter.

“Biochip” refers to a solid substrate having a generally planar surfaceto which an adsorbent is attached. Frequently, the surface of thebiochip comprises a plurality of addressable locations, each of whichlocation has the adsorbent bound there. Biochips can be adapted toengage a probe interface and, therefore, function as probes.

“Protein biochip” refers to a biochip adapted for the capture ofpolypeptides. Many protein biochips are described in the art. Theseinclude, for example, protein biochips produced by Ciphergen Biosystems(Fremont, Calif.), Packard BioScience Company (Meriden Conn.), Zyomyx(Hayward, Calif.) and Phylos (Lexington, Mass.). Examples of suchprotein biochips are described in the following patents or patentapplications: U.S. Pat. No. 6,225,047 (Hutchens and Yip, “Use ofretentate chromatography to generate difference maps,” May 1, 2001);International publication WO 99/51773 (Kuimelis and Wagner, “Addressableprotein arrays,” Oct. 14, 1999); U.S. Pat. No. 6,329,209 (Wagner et al.,“Arrays of protein-capture agents and methods of use thereof,” Dec. 11,2001) and International publication WO 00/56934 (Englert et al.,“Continuous porous matrix arrays,” Sep. 28, 2000).

Protein biochips produced by Ciphergen Biosystems comprise surfaceshaving chromatographic or biospecific adsorbents attached thereto ataddressable locations. Ciphergen ProteinChip® arrays include NP20, H4,HSO, SAX-2, WCX-2, CM-10, IMAC-3, IMAC-30, LS AX-30, LWCX-30, IMAC-40,PS-10, PS-20 and PG-20. These protein biochips comprise an aluminumsubstrate in the form of a strip. The surface of the strip is coatedwith silicon dioxide.

In the case of the NP-20 biochip, silicon oxide functions as ahydrophilic adsorbent to capture hydrophilic proteins. H4, HSO, SAX-2,WCX-2, CM-10, IMAC-3, IMAC-30, PS-10 and PS-20 biochips further comprisea functionalized, cross-linked polymer in the form of a hydrogelphysically attached to the surface of the biochip or covalently attachedthrough a silane to the surface of the biochip. The H4 biochip hasisopropyl functionalities for hydrophobic binding. The HSO biochip hasnonylphenoxy-poly(ethylene glycol)methacrylate for hydrophobic binding.The SAX-2 biochip has quaternary ammonium functionalities for anionexchange. The WCX-2 and CM-10 biochips have carboxylate functionalitiesfor cation exchange. The IMAC-3 and IMAC-30 biochips have nitriloaceticacid functionalities that adsorb transition metal ions, such as Cu++ andNi++, by chelation. These immobilized metal ions allow adsorption ofpeptide and proteins by coordinate bonding. The PS-10 biochip hascarboimidizole functional groups that can react with groups on proteinsfor covalent binding. The PS-20 biochip has epoxide functional groupsfor covalent binding with proteins. The PS-series biochips are usefulfor binding biospecific adsorbents, such as antibodies, receptors,lectins, heparin, Protein A, biotin/streptavidin and the like, to chipsurfaces where they function to specifically capture analytes from asample. The PG-20 biochip is a PS-20 chip to which Protein G isattached. The LSAX-30 (anion exchange), LWCX-30 (cation exchange) andIMAC-40 (metal chelate) biochips have functionalized latex beads ontheir surfaces. Such biochips are further described in: WO 00/66265(Rich et al., “Probes for a Gas Phase Ion Spectrometer,” Nov. 9, 2000);WO 00/67293 (Beecher et al., “Sample Holder with Hydrophobic Coating forGas Phase Mass Spectrometer,” Nov. 9, 2000); U.S. patent applicationUS20030032043A1 (Pohl and Papanu, “Latex Based Adsorbent Chip,” Jul. 16,2002) and U.S. patent application 60/350,110 (Um et al., “HydrophobicSurface Chip,” Nov. 8, 2001).

Upon capture on a biochip, analytes can be detected by a variety ofdetection methods selected from, for example, a gas phase ionspectrometry method, an optical method, an electrochemical method,atomic force microscopy and a radio frequency method. Gas phase ionspectrometry methods are described herein. Of particular interest is theuse of mass spectrometry and, in particular, SELDI. Optical methodsinclude, for example, detection of fluorescence, luminescence,chemiluminescence, absorbance, reflectance, transmittance, birefringenceor refractive index (e.g., surface plasmon resonance, ellipsometry, aresonant mirror method, a grating coupler waveguide method orinterferometry). Optical methods include microscopy (both confocal andnon-confocal), imaging methods and non-imaging methods. Immunoassays invarious formats (e.g., ELISA) are popular methods for detection ofanalytes captured on a solid phase. Electrochemical methods includevoltametry and amperometry methods. Radio frequency methods includemultipolar resonance spectroscopy.

“Marker” in the context of the present invention refers to a polypeptide(of a particular apparent molecular weight), which is differentiallypresent in a sample taken from patients having human cancer as comparedto a comparable sample taken from control subjects (e.g., a person witha negative diagnosis or undetectable cancer, normal or healthy subject).The term “biomarker” is used interchangeably with the term “marker.”

The term “measuring” means methods which include detecting the presenceor absence of marker(s) in the sample, quantifying the amount ofmarker(s) in the sample, and/or qualifying the type of biomarker.Measuring can be accomplished by methods known in the art and thosefurther described herein, including but not limited to SELDI andimmunoassay. Any suitable methods can be used to detect and measure oneor more of the markers described herein. These methods include, withoutlimitation, mass spectrometry (e.g., laser desorption/ionization massspectrometry), fluorescence (e.g. sandwich immunoassay), surface plasmonresonance, ellipsometry and atomic force microscopy.

The phrase “differentially present” refers to differences in thequantity and/or the frequency of a marker present in a sample taken frompatients having human cancer as compared to a control subject. Forexample, the IAIH4 fragment is present at an elevated level in samplesof ovarian cancer patients compared to samples from control subjects. Incontrast, Apo A1 and transthyretin described herein are present at adecreased level in samples of ovarian cancer patients compared tosamples from control subjects. Furthermore, a marker can be apolypeptide, which is detected at a higher frequency or at a lowerfrequency in samples of human cancer patients compared to samples ofcontrol subjects. A marker can be differentially present in terms ofquantity, frequency or both.

A polypeptide is differentially present between two samples if theamount of the polypeptide in one sample is statistically significantlydifferent from the amount of the polypeptide in the other sample. Forexample, a polypeptide is differentially present between the two samplesif it is present at least about 120%, at least about 130%, at leastabout 150%, at least about 180%, at least about 200%, at least about300%, at least about 500%, at least about 700%, at least about 900%, orat least about 1000% greater than it is present in the other sample, orif it is detectable in one sample and not detectable in the other.

Alternatively or additionally, a polypeptide is differentially presentbetween two sets of samples if the frequency of detecting thepolypeptide in the ovarian cancer patients' samples is statisticallysignificantly higher or lower than in the control samples. For example,a polypeptide is differentially present between the two sets of samplesif it is detected at least about 120%, at least about 130%, at leastabout 150%, at least about 180%, at least about 200%, at least about300%, at least about 500%, at least about 700%, at least about 900%, orat least about 1000% more frequently or less frequently observed in oneset of samples than the other set of samples.

“Diagnostic” means identifying the presence or nature of a pathologiccondition, i.e., ovarian cancer. Diagnostic methods differ in theirsensitivity and specificity. The “sensitivity” of a diagnostic assay isthe percentage of diseased individuals who test positive (percent of“true positives”). Diseased individuals not detected by the assay are“false negatives.” Subjects who are not diseased and who test negativein the assay, are termed “true negatives.” The “specificity” of adiagnostic assay is 1 minus the false positive rate, where the “falsepositive” rate is defined as the proportion of those without the diseasewho test positive. While a particular diagnostic method may not providea definitive diagnosis of a condition, it suffices if the methodprovides a positive indication that aids in diagnosis.

A “test amount” of a marker refers to an amount of a marker present in asample being tested. A test amount can be either in absolute amount(e.g., μg/ml) or a relative amount (e.g., relative intensity ofsignals).

A “diagnostic amount” of a marker refers to an amount of a marker in asubject's sample that is consistent with a diagnosis of ovarian cancer.A diagnostic amount can be either in absolute amount (e.g., μg/ml) or arelative amount (e.g., relative intensity of signals).

A “control amount” of a marker can be any amount or a range of amount,which is to be compared against a test amount of a marker. For example,a control amount of a marker can be the amount of a marker in a personwithout ovarian cancer. A control amount can be either in absoluteamount (e.g., μg/ml) or a relative amount (e.g., relative intensity ofsignals).

“Antibody” refers to a polypeptide ligand substantially encoded by animmunoglobulin gene or immunoglobulin genes, or fragments thereof, whichspecifically binds and recognizes an epitope (e.g., an antigen). Therecognized immunoglobulin genes include the kappa and lambda light chainconstant region genes, the alpha, gamma, delta, epsilon and mu heavychain constant region genes, and the myriad immunoglobulin variableregion genes. Antibodies exist, e.g., as intact immunoglobulins or as anumber of well-characterized fragments produced by digestion withvarious peptidases. This includes, e.g., Fab′ and F(ab)′₂ fragments. Theterm “antibody,” as used herein, also includes antibody fragments eitherproduced by the modification of whole antibodies or those synthesized denovo using recombinant DNA methodologies. It also includes polyclonalantibodies, monoclonal antibodies, chimeric antibodies, humanizedantibodies, or single chain antibodies. “Fc” portion of an antibodyrefers to that portion of an immunoglobulin heavy chain that comprisesone or more heavy chain constant region domains, CH₁, CH₂ and CH₃, butdoes not include the heavy chain variable region.

“Managing subject treatment” refers to the behavior of the clinician orphysician subsequent to the determination of ovarian cancer status. Forexample, if the result of the methods of the present invention isinconclusive or there is reason that confirmation of status isnecessary, the physician may order more tests. Alternatively, if thestatus indicates that surgery is appropriate, the physician may schedulethe patient for surgery. Likewise, if the status is negative, e.g., latestage ovarian cancer or if the status is acute, no further action may bewarranted. Furthermore, if the results show that treatment has beensuccessful, no further management may be necessary.

The term “stage” or “cancer stage” is intended to mean a classificationof ovarian cancer that is based on the size, invasiveness, progression,migration, etc. of cancer in a subject. The stages of ovarian cancer arewell defined. Stage I refers to ovarian cancer wherein the cancer isstill contained within the ovary (or ovaries). Specifically, stage IAcancer has developed in one ovary, and the tumor is confined to theinside of the ovary. There is no cancer on the outer surface of theovary. Laboratory examination of washings from the abdomen and pelvisdid not find any cancer cells. Stage IB cancer has developed within bothovaries without any tumor on their outer surfaces. Laboratoryexamination of washings from the abdomen and pelvis did not find anycancer cells. Stage IC cancer is present in one or both ovaries and 1 ormore of the following are present: cancer on the outer surface of atleast one of the ovaries; in the case of cystic tumors (fluid-filledtumors), the capsule (outer wall of the tumor) has ruptured (burst); orlaboratory examination found cancer cells in fluid or washings from theabdomen.

Stage II cancer is in one or both ovaries and has involved other organs(such as the uterus, fallopian tubes, bladder, the sigmoid colon, or therectum) within the pelvis. Specifically, stage HA cancer has spread toor has actually invaded the uterus or the fallopian tubes, or both.Laboratory examination of washings from the abdomen did not find anycancer cells. Stage IIB cancer has spread to other nearby pelvic organssuch as the bladder, the sigmoid colon, or the rectum. Laboratoryexamination of fluid from the abdomen did not find any cancer cells.Stage IIC cancer has spread to pelvic organs as in stages IIA or IIB andlaboratory examination of the washings from the abdomen found evidenceof cancer cells.

Stage III cancer involves 1 or both ovaries, and 1 or both of thefollowing are present: (1) cancer has spread beyond the pelvis to thelining of the abdomen; (2) cancer has spread to lymph nodes. The canceris Stage IIIA if, during the staging operation, the surgeon can seecancer involving the ovary or ovaries, but no cancer is grossly visible(can be seen without using a microscope) in the abdomen and the cancerhas not spread to lymph nodes. However, when biopsies are checked undera microscope, tiny deposits of cancer are found in the lining of theupper abdomen. Stage IIIB cancer is in one or both ovaries, and depositsof cancer large enough for the surgeon to see, but smaller than 2 cm(about ¾ inch) across, are present in the abdomen. Cancer has not spreadto the lymph nodes. For a cancer to be stage RIC the cancer is in one orboth ovaries, and one or both of the following are present: cancer hasspread to lymph nodes and/or deposits of cancer larger than 2 cm (about¾ inch) across are seen in the abdomen.

Stage IV cancer is the most advanced stage of ovarian cancer. The canceris in one or both ovaries. Distant metastasis (spread of the cancer tothe inside of the liver, the lungs, or other organs located outside ofthe peritoneal cavity) has occurred. Finding ovarian cancer cells inpleural fluid (from the cavity that surrounds the lungs) is alsoevidence of stage IV disease.

As used herein, the term “recurrent ovarian cancer” is intended to meanthat the disease has come back (recurred) after completion of treatment.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides sensitive and quick methods and kits thatare useful for determining if a patient has ovarian cancer by measuringand identifying particular biomarkers. Specifically, the panels ofbiomarkers identified herein are useful to see if a subject has earlystage ovarian cancer. The detection and measurement of these biomarkersin patient samples provides information that diagnosticians cancorrelate to the cancer status of a patient.

More specifically, two panels of biomarkers were discovered andcharacterized, in accordance with the methods described herein as. Thefirst panel comprises (i) apolipoprotein A1 (ApoA1), (ii) transthyretin(TTR), (iii) inter-alpha (globulin) inhibitor H4 (plasmaKallikrein-sensitive glycoprotein) (ITIH4), (iv) transferrin (TFR) and(v) CA125. The second biomarker panel comprises (i) apolipoprotein A1(ApoA1), (ii) transthyretin (TTR), (iii) CTAPIII, and (iv) CA125.

Some of these biomarkers have been disclosed in PCT/US2005/010783 (WO2005/098447); US Patent Application Publication 2005/0059013;PCT/US03/00531 (WO03/057014); PCT/US2003/024636 (WO 2004/012588);PCT/U506/08578; and U.S. patent application Ser. No. 11/373,833, all ofwhich documents are incorporated herein by reference in their entirety.

These biomarkers assess a patient's ovarian cancer status after havingdeveloped ovarian cancer and could potentially provide additionalinformation to physicians for clinical decision-making. This issupported by Receiver-Operating Characteristic (ROC) curve analysis inan independent validation. For example, several large-scale studies havesuggested that ovarian cancer patients with surgical procedures operatedby gynecological oncologists tend to have a better long-term survival.However, other studies concluded that currently only about one third ofovarian cancer patients undergoing surgical procedures in the US aretreated by gynecological oncologists. With the current total number ofgynecological oncologists available, it is still not practical to haveall patients undergoing surgery for suspected ovarian cancer be operatedby gynecologic oncologists. The biomarkers have the potential to be usedto identify patients with the lower probability of surviving ovariancancer and recommend them for treatment by gynecologic oncologists.

High-throughput protein profiling combined with effective use ofbioinformatics tools provides a useful approach to screening for cancermarkers. Briefly, the system used in the present invention utilizeschromatographic ProteinChip® Arrays to assay samples using SELDI(Surface Enhanced Laser Desorption/Ionization). Proteins bound to thearrays are read in a ProteinChip®Reader, a time-of-flight massspectrometer.

The present invention is based upon the discovery of protein markersthat are differentially present in samples of ovarian cancer patientsand control subjects, and the application of this discovery in methodsand kits for determining ovarian cancer status. Specifically, the panelsof markers described herein are particularly useful in diagnosing earlystage ovarian cancer. These protein markers are found in samples fromovarian cancer patients at levels that are different than the levels insamples from women in whom human cancer is undetectable. Accordingly,the amount of these biomarkers found in a test sample compared to acontrol, or the presence or absence of one or more markers in the testsample provides useful information regarding the ovarian cancer statusof the patient.

I. Description of the Biomarkers

1. IAIH4 Fragments

Other biomarkers that are useful in the methods of the present inventionone or more of a closely related set of cleavage fragments ofinter-α-trypsin inhibitor heavy chain H4 precursor, also referred toalternatively herein as “ITIH4 fragments.” ITIH4 fragments are describedas biomarkers for ovarian cancer in US patent publication 2005-0059013A1, International Patent Publication WO 2005/098447 and Fung et al.,Int. J. Cancer 115:783-789 (2005). ITIH4 fragments can be selected fromthe group consisting of ITIH4 fragment no. 1, ITIH4 fragment no. 2, andITIH4 fragment no. 3.

The amino acid sequences of the ITIH4 fragments were determined to be:ITIH4 fragment 1 (SEQ ID NO: 5): MNFRPGVLSSRQLGLPGPPDVPDHAAYHPF ITIH4fragment 2 (SEQ ID NO: 6): PGVLSSRQLGLPGPPDVPDHAAYHPF ITIH4 fragment 3(SEQ ID NO: 7): GVLSSRQLGLPGPPDVPDHAAYHPF. The present invention alsoincludes all other known fragments of ITIHA4.

ITIH4 precursor is a 930 amino acid protein (SwissProt Q14624). ITIH4fragment 1 spans amino acids 658-687 of human ITIH4 precursor. ITIH4fragment 2 spans amino acids 662-687 of ITIH4 precursor. ITIH4 fragment3 spans amino acids 663-687 of ITIH4 precursor.

Additionally, preferred methods of the present invention include the useof modified forms of ITIH4 fragment. Modification of ITIH4 fragment mayinclude the post-translational addition of various chemical groups, forexample, glycosylation, lipidation, cysteinylation, andglutathionylation.

2. CTAPIII

Another biomarker that is useful in the methods of the present inventionis CTAP-III (connective tissue activating peptide III), derived fromplatelet basic protein. CTAP-III is described as a biomarker for ovariancancer in U.S. provisional patent application 60/693,324, filed Jun. 22,2005 (Zhang et al.). CTAP-III is an 85 amino acid protein (SwissProtP02775) (SEQ ID NO: 8). CTAP-III is recognized by antibodies availablefrom, e.g., Chemicon International (catalog 1484P) (www.chemicon.com,Temecula, Calif.) CTAP-III is a fragment of platelet basic protein andincludes amino acids 44-128 of platelet basic protein.

3. Transthyretin

Transthyretin, also called “pre-albumin” is another biomarker that isuseful in the methods of the present invention. Transthyretin andvariants thereof are described as biomarkers for ovarian cancer in USpatent publication 2005-0059013 A1 and International Patent PublicationWO 2005/098447. Unmodified transthyretin is a 127 amino acid proteinderiving from a 147 amino acid precursor (SwissProt Accession No.P02766) (SEQ ID NO: 9). The transthyretin biomarkers of the presentinvention include any or all of unmodified transthyretin and variousmodified forms. Transthyretin is recognized by antibodies availablefrom, e.g., Dako (catalog A0002) (www.dako.com, Glostrup, Denmark).

In mass spectra of serum, transthyretin appears as a cluster of peaksaround 13.9K Daltons. This cluster includes several forms oftransthyretin including unmodified transthyretin, S-sulfonatedthransthyretin, S-cysteinylated transthyretin, S-Gly-Cys transthyretinand S-glutathionylated transthryetin. Any and/or all of these is usefulas a biomarker for ovarian cancer. However, the S-cysteinylated versionrepresents the dominant form in the spectrum and is a preferredbiomarker when using mass spectrometry. Another variant of transthyretinuseful as a biomarker is transthyretin ΔN10.

4. Transferrin

Another biomarker that is useful in the methods of the present inventionis transferrrin. Transferrrin is described as a biomarker for ovariancancer in US patent publication 2005-0214760 A1. Transferrin is a 679amino acid protein derived from a 698 amino acid precursor (GenBankAccession No. NP_(—)001054 GI:4557871; SwissProt Accession No. P02787)(SEQ ID NO: 10). Transferrin is recognized by antibodies available from,e.g., Dako (catalog A006) (www.dako.com, Glostrup, Denmark). Transferrinis glycosylated. Therefore, the measured molecular weight is higher thanthe theoretical weight, which does not take glycosylation into account.

5. Apolipoprotein A1

Another biomarker that is useful in the methods of the present inventionis apolipoprotein A1, also referred to as Apo A1. Apo A1 is described asa biomarker for ovarian cancer in US patent publication 2005-0059013 A1and International Patent Publication WO 2005/098447. Apo A1 is a 243amino acid protein derived from a 267 amino acid precursor (SwissProtAccession No. P02647) SEQ ID NO: 12). Apo A1 is recognized by antibodiesavailable from, e.g., EMD Biosciences, Inc. (catalog 178474)(www.emdbiosciences.com/home.asp, San Diego, Calif.). ApoA1 can bevisualized on H50 arrays or IMAC30 or IMAC50 arrays, but ispreferentially visualized on H50 arrays.

Preferred methods of the present invention include the use of modifiedforms of Apo A1, such as C-terminal truncation of Apo AI (amino acids 1thru 190-200). Modification of Apo A1 may include the post-translationaladdition of various chemical groups, for example, glycosylation andlipidation.

Because the biomarker of this invention is characterized bymass-to-charge ratio, binding properties and spectral shape, they can bedetected by mass spectrometry without knowing their specific identity.However, if desired, biomarkers whose identity is not determined can beidentified by, for example, determining the amino acid sequence of thepolypeptides. For example, a biomarker can be peptide-mapped with anumber of enzymes, such as trypsin or V8 protease, and the molecularweights of the digestion fragments can be used to search databases forsequences that match the molecular weights of the digestion fragmentsgenerated by the various enzymes. Alternatively, protein biomarkers canbe sequenced using tandem MS technology. In this method, the protein isisolated by, for example, gel electrophoresis. A band containing thebiomarker is cut out and the protein is subject to protease digestion.Individual protein fragments are separated by a first mass spectrometer.The fragment is then subjected to collision-induced cooling, whichfragments the peptide and produces a polypeptide ladder. A polypeptideladder is then analyzed by the second mass spectrometer of the tandemMS. The difference in masses of the members of the polypeptide ladderidentifies the amino acids in the sequence. An entire protein can besequenced this way, or a sequence fragment can be subjected to databasemining to find identity candidates.

U.S. patent application Ser. No. 11/373,833, filed Mar. 10, 2006 ishereby incorporated by reference in its entirety.

It has been found that proteins frequently exist in a sample in aplurality of different forms characterized by a detectably differentmass. These forms can result from either, or both, of pre- andpost-translational modification. Pre-translational modified formsinclude allelic variants, slice variants and RNA editing forms.Post-translationally modified forms include forms resulting fromproteolytic cleavage (e.g., fragments of a parent protein),glycosylation, phosphorylation, lipidation, oxidation, methylation,cystinylation, sulphonation and acetylation. The collection of proteinsincluding a specific protein and all modified forms of it is referred toherein as a “protein cluster.” The collection of all modified forms of aspecific protein, excluding the specific protein, itself, is referred toherein as a “modified protein cluster.” Modified forms of the biomarkerof this invention also may be used, themselves, as biomarkers. Incertain cases the modified forms may exhibit better discriminatory powerin diagnosis than the specific forms set forth herein.

Modified forms of a biomarker can be initially detected by anymethodology that can detect and distinguish the modified from thebiomarker. A preferred method for initial detection involves firstcapturing the biomarker and modified forms of it, e.g., with biospecificcapture reagents, and then detecting the captured proteins by massspectrometry. More specifically, the proteins are captured usingbiospecific capture reagents, such as antibodies, aptamers or Affibodiesthat recognize the biomarker and modified forms of it. This method alsowill also result in the capture of protein interactors that are bound tothe proteins or that are otherwise recognized by antibodies and that,themselves, can be biomarkers. In certain embodiments, the biospecificcapture reagents are bound to a solid phase. Then, the captured proteinscan be detected by SELDI mass spectrometry or by eluting the proteinsfrom the capture reagent and detecting the eluted proteins bytraditional MALDI or by SELDI. The use of mass spectrometry isespecially attractive because it can distinguish and quantify modifiedforms of a protein based on mass and without the need for labeling.

Preferably, the biospecific capture reagent is bound to a solid phase,such as a bead, a plate, a membrane or a chip. Methods of couplingbiomolecules, such as antibodies, to a solid phase are well known in theart. They can employ, for example, bifunctional linking agents, or thesolid phase can be derivatized with a reactive group, such as an epoxideor an imidizole, that will bind the molecule on contact. Biospecificcapture reagents against different target proteins can be mixed in thesame place, or they can be attached to solid phases in differentphysical or addressable locations. For example, one can load multiplecolumns with derivatized beads, each column able to capture a singleprotein cluster. Alternatively, one can pack a single column withdifferent beads derivatized with capture reagents against a variety ofprotein clusters, thereby capturing all the analytes in a single place.Accordingly, antibody-derivatized bead-based technologies, such as xMAPtechnology of Luminex (Austin, Tex.) can be used to detect the proteinclusters. However, the biospecific capture reagents must be specificallydirected toward the members of a cluster in order to differentiate them.

In yet another embodiment, the surfaces of biochips can be derivatizedwith the capture reagents directed against protein clusters either inthe same location or in physically different addressable locations. Oneadvantage of capturing different clusters in different addressablelocations is that the analysis becomes simpler.

After identification of modified forms of a protein and correlation withthe clinical parameter of interest, the modified form can be used as abiomarker in any of the methods of this invention. At this point,detection of the modified form can be accomplished by any specificdetection methodology including affinity capture followed by massspectrometry, or traditional immunoassay directed specifically themodified form. Immunoassay requires biospecific capture reagents, suchas antibodies, to capture the analytes. Furthermore, if the assay mustbe designed to specifically distinguish protein and modified forms ofprotein. This can be done, for example, by employing a sandwich assay inwhich one antibody captures more than one form and second, distinctlylabeled antibodies, specifically bind, and provide distinct detectionof, the various forms. Antibodies can be produced by immunizing animalswith the biomolecules. This invention contemplates traditionalimmunoassays including, for example, sandwich immunoassays includingELISA or fluorescence-based immunoassays, as well as other enzymeimmunoassays.

II. Test Samples

A) Subject Types

Samples are collected from subjects, e.g., women, who want to establishovarian cancer status. The subjects may be women showing no healthissues or predisposition to cancer and are being tested as part of anormal examination. The subjects may be women who have been determinedto have a high risk of ovarian cancer based on their family history.Other patients include women who have ovarian cancer or women diagnosedwith a pelvic mass and the test is being used to determine theeffectiveness of therapy or treatment they are receiving. Also, patientscould include healthy women who are having a test as part of a routineexamination, or to establish baseline levels of the biomarkers. Samplesmay be collected from women who had been diagnosed with ovarian cancerand received treatment to eliminate the cancer, or perhaps are inremission.

B) Types of Sample and Preparation of the Sample

The markers can be measured in different types of biological samples.The sample is preferably a biological fluid sample. Examples of abiological fluid sample useful in this invention include blood, bloodserum, plasma, vaginal secretions, urine, ovarian cyst fluid, tears,saliva, etc. Because all of the markers are found in blood serum, bloodserum is a preferred sample source for embodiments of the invention.

If desired, the sample can be prepared to enhance detectability of themarkers. For example, to increase the detectability of markers, a bloodserum sample from the subject can be preferably fractionated by, e.g.,Cibacron blue agarose chromatography and single stranded DNA affinitychromatography, anion exchange chromatography, affinity chromatography(e.g., with antibodies) and the like. The method of fractionationdepends on the type of detection method used. Any method that enrichesfor the protein of interest can be used. Sample preparations, such aspre-fractionation protocols, are optional and may not be necessary toenhance detectability of markers depending on the methods of detectionused. For example, sample preparation may be unnecessary if antibodiesthat specifically bind markers are used to detect the presence ofmarkers in a sample.

Typically, sample preparation involves fractionation of the sample andcollection of fractions determined to contain the biomarkers. Methods ofpre-fractionation include, for example, size exclusion chromatography,ion exchange chromatography, heparin chromatography, affinitychromatography, sequential extraction, gel electrophoresis and liquidchromatography. The analytes also may be modified prior to detection.These methods are useful to simplify the sample for further analysis.For example, it can be useful to remove high abundance proteins, such asalbumin, from blood before analysis. Examples of methods offractionation are described in PCT/US03/00531 (incorporated herein inits entirety).

Preferably, the sample is pre-fractionated by anion exchangechromatography. Anion exchange chromatography allows pre-fractionationof the proteins in a sample roughly according to their chargecharacteristics. For example, a Q anion-exchange resin can be used(e.g., Q HyperD F, Biosepra), and a sample can be sequentially elutedwith eluants having different pH's. Anion exchange chromatography allowsseparation of biomolecules in a sample that are more negatively chargedfrom other types of biomolecules. Proteins that are eluted with aneluant having a high pH is likely to be weakly negatively charged, and afraction that is eluted with an eluant having a low pH is likely to bestrongly negatively charged. Thus, in addition to reducing complexity ofa sample, anion exchange chromatography separates proteins according totheir binding characteristics.

In preferred embodiments, the serum samples are fractionated via anionexchange chromatography. Signal suppression of lower abundance proteinsby high abundance proteins presents a significant challenge to SELDImass spectrometry. Fractionation of a sample reduces the complexity ofthe constituents of each fraction. This method can also be used toattempt to isolate high abundance proteins into a fraction, and therebyreduce its signal suppression effect on lower abundance proteins. Anionexchange fractionation separates proteins by their isoelectric point(pI). Proteins are comprised of amino acids, which are ambivalent-theircharge changes based on the pH of the environment to which they areexposed. A protein's pI is the pH at which the protein has no netcharge. A protein assumes a neutral charge when the pH of theenvironment is equivalent to pI of the protein. When the pH rises abovethe pI of the protein, the protein assumes a net negative charge.Similarly, when the pH of the environment falls below the pI of theprotein, the protein has a net positive charge. The serum samples werefractionated according to the protocol set forth in the Examples belowto obtain the markers described herein.

After capture on anion exchange, proteins were eluted in a series ofstep washes at pH 9, pH 7, pH 5, pH 4 and pH 3. A panel of threepotential biomarkers was discovered by UMSA analysis of profiling dataof three fractions (pH 9/flow through, pH 4, and organic solvent). Twoof the peaks were from fraction pH 4 at m/z of 12828 and 28043, bothdown-regulated in the cancer group, and the third was from fraction pH9/flow through at m/z of 3272, up-regulated in the cancer group. Allbound to the immobilized metal affinity chromatography array chargedwith copper ions (IMAC3-Cu).

Biomolecules in a sample can also be separated by high-resolutionelectrophoresis, e.g., one or two-dimensional gel electrophoresis. Afraction containing a marker can be isolated and further analyzed by gasphase ion spectrometry. Preferably, two-dimensional gel electrophoresisis used to generate two-dimensional array of spots of biomolecules,including one or more markers. See, e.g., Jungblut and Thiede, MassSpectr. Rev. 16:145-162 (1997). The two-dimensional gel electrophoresiscan be performed using methods known in the art. See, e.g., Deutschered., Methods In Enzymology vol. 182. Typically, biomolecules in a sampleare separated by, e.g., isoelectric focusing, during which biomoleculesin a sample are separated in a pH gradient until they reach a spot wheretheir net charge is zero (i.e., isoelectric point). This firstseparation step results in one-dimensional array of biomolecules. Thebiomolecules in one-dimensional array is further separated using atechnique generally distinct from that used in the first separationstep. For example, in the second dimension, biomolecules separated byisoelectric focusing are further separated using a polyacrylamide gel,such as polyacrylamide gel electrophoresis in the presence of sodiumdodecyl sulfate (SDS-PAGE). SDS-PAGE gel allows further separation basedon molecular mass of biomolecules. Typically, two-dimensional gelelectrophoresis can separate chemically different biomolecules in themolecular mass range from 1000-200,000 Da within complex mixtures. ThepI range of these gels is about 3-10 (wide range gels).

Biomolecules in the two-dimensional array can be detected using anysuitable methods known in the art. For example, biomolecules in a gelcan be labeled or stained (e.g., Coomassie Blue or silver staining). Ifgel electrophoresis generates spots that correspond to the molecularweight of one or more markers of the invention, the spot can be furtheranalyzed by gas phase ion spectrometry. For example, spots can beexcised from the gel and analyzed by gas phase ion spectrometry.Alternatively, the gel containing biomolecules can be transferred to aninert membrane by applying an electric field. Then a spot on themembrane that approximately corresponds to the molecular weight of amarker can be analyzed by gas phase ion spectrometry. In gas phase ionspectrometry, the spots can be analyzed using any suitable techniques,such as MALDI or SELDI (e.g., using ProteinChip® array) as describedherein.

Prior to gas phase ion spectrometry analysis, it may be desirable tocleave biomolecules in the spot into smaller fragments using cleavingreagents, such as proteases (e.g., trypsin). The digestion ofbiomolecules into small fragments provides a mass fingerprint of thebiomolecules in the spot, which can be used to determine the identity ofmarkers if desired.

High performance liquid chromatography (HPLC) can also be used toseparate a mixture of biomolecules in a sample based on their differentphysical properties, such as polarity, charge and size. HPLC instrumentstypically consist of a reservoir of mobile phase, a pump, an injector, aseparation column, and a detector. Biomolecules in a sample areseparated by injecting an aliquot of the sample onto the columnDifferent biomolecules in the mixture pass through the column atdifferent rates due to differences in their partitioning behaviorbetween the mobile liquid phase and the stationary phase. A fractionthat corresponds to the molecular weight and/or physical properties ofone or more markers can be collected. The fraction can then be analyzedby gas phase ion spectrometry to detect markers. For example, the spotscan be analyzed using either MALDI or SELDI (e.g., using ProteinChip®array) as described herein.

Optionally, a marker can be modified before analysis to improve itsresolution or to determine its identity. For example, the markers may besubject to proteolytic digestion before analysis. Any protease can beused. Proteases, such as trypsin, that are likely to cleave the markersinto a discrete number of fragments are particularly useful. Thefragments that result from digestion function as a fingerprint for themarkers, thereby enabling their detection indirectly. This isparticularly useful where there are markers with similar molecularmasses that might be confused for the marker in question. Also,proteolytic fragmentation is useful for high molecular weight markersbecause smaller markers are more easily resolved by mass spectrometry.In another example, biomolecules can be modified to improve detectionresolution. For instance, neuraminidase can be used to remove terminalsialic acid residues from glycoproteins to improve binding to an anionicadsorbent (e.g., cationic exchange ProteinChip® arrays) and to improvedetection resolution. In another example, the markers can be modified bythe attachment of a tag of particular molecular weight that specificallybind to molecular markers, further distinguishing them. Optionally,after detecting such modified markers, the identity of the markers canbe further determined by matching the physical and chemicalcharacteristics of the modified markers in a protein database (e.g.,SwissProt).

III. Capture of Markers

Biomarkers are preferably captured with capture reagents immobilized toa solid support, such as any biochip described herein, a multiwellmicrotiter plate or a resin. In particular, the biomarkers of thisinvention are preferably captured on SELDI protein biochips. Capture canbe on a chromatographic surface or a biospecific surface. Any of theSELDI protein biochips comprising reactive surfaces can be used tocapture and detect the biomarkers of this invention. However, thebiomarkers of this invention bind well to immobilized metal chelates.The IMAC-3 and IMAC 30 biochips, which nitriloacetic acidfunctionalities that adsorb transition metal ions, such as Cu⁺⁺ andNi⁺⁺, by chelation, are the preferred SELDI biochips for capturing thebiomarkers of this invention. Any of the SELDI protein biochipscomprising reactive surfaces can be used to capture and detect thebiomarkers of this invention. These biochips can be derivatized with theantibodies that specifically capture the biomarkers, or they can bederivatized with capture reagents, such as protein A or protein G thatbind immunoglobulins. Then the biomarkers can be captured in solutionusing specific antibodies and the captured markers isolated on chipthrough the capture reagent.

In general, a sample containing the biomarkers, such as serum, is placedon the active surface of a biochip for a sufficient time to allowbinding. Then, unbound molecules are washed from the surface using asuitable eluant, such as phosphate buffered saline. In general, the morestringent the eluant, the more tightly the proteins must be bound to beretained after the wash. The retained protein biomarkers now can bedetected by appropriate means.

IV. Detection and Measurement of Markers

Once captured on a substrate, e.g., biochip or antibody, any suitablemethod can be used to measure a marker or markers in a sample. Forexample, markers can be detected and/or measured by a variety ofdetection methods including for example, gas phase ion spectrometrymethods, optical methods, electrochemical methods, atomic forcemicroscopy and radio frequency methods. Using these methods, one or moremarkers can be detected.

A) SELDI

One preferred method of detection and/or measurement of the biomarkersuses mass spectrometry and, in particular, “Surface-enhanced laserdesorption/ionization” or “SELDI”. SELDI refers to a method ofdesorption/ionization gas phase ion spectrometry (e.g., massspectrometry) in which the analyte is captured on the surface of a SELDIprobe that engages the probe interface. In “SELDI MS,” the gas phase ionspectrometer is a mass spectrometer. SELDI technology is described inmore detail above.

B) Immunoassay

In another embodiment, an immunoassay can be used to detect and analyzemarkers in a sample. This method comprises: (a) providing an antibodythat specifically binds to a marker; (b) contacting a sample with theantibody; and (c) detecting the presence of a complex of the antibodybound to the marker in the sample.

An immunoassay is an assay that uses an antibody to specifically bind anantigen (e.g., a marker). The immunoassay is characterized by the use ofspecific binding properties of a particular antibody to isolate, target,and/or quantify the antigen. The phrase “specifically (or selectively)binds” to an antibody or “specifically (or selectively) immunoreactivewith,” when referring to a protein or peptide, refers to a bindingreaction that is determinative of the presence of the protein in aheterogeneous population of proteins and other biologics. Thus, underdesignated immunoassay conditions, the specified antibodies bind to aparticular protein at least two times the background and do notsubstantially bind in a significant amount to other proteins present inthe sample. Specific binding to an antibody under such conditions mayrequire an antibody that is selected for its specificity for aparticular protein. For example, polyclonal antibodies raised to amarker from specific species such as rat, mouse, or human can beselected to obtain only those polyclonal antibodies that arespecifically immunoreactive with that marker and not with otherproteins, except for polymorphic variants and alleles of the marker.This selection may be achieved by subtracting out antibodies thatcross-react with the marker molecules from other species.

Using the purified markers or their nucleic acid sequences, antibodiesthat specifically bind to a marker can be prepared using any suitablemethods known in the art. See, e.g., Coligan, Current Protocols inImmunology (1991); Harlow & Lane, Antibodies: A Laboratory Manual(1988); Goding, Monoclonal Antibodies: Principles and Practice (2d ed.1986); and Kohler & Milstein, Nature 256:495-497 (1975). Such techniquesinclude, but are not limited to, antibody preparation by selection ofantibodies from libraries of recombinant antibodies in phage or similarvectors, as well as preparation of polyclonal and monoclonal antibodiesby immunizing rabbits or mice (see, e.g., Huse et al., Science246:1275-1281 (1989); Ward et al., Nature 341:544-546 (1989)). Typicallya specific or selective reaction will be at least twice backgroundsignal or noise and more typically more than 10 to 100 times background.

Generally, a sample obtained from a subject can be contacted with theantibody that specifically binds the marker. Optionally, the antibodycan be fixed to a solid support to facilitate washing and subsequentisolation of the complex, prior to contacting the antibody with asample. Examples of solid supports include glass or plastic in the formof, e.g., a microtiter plate, a stick, a bead, or a microbead.Antibodies can also be attached to a probe substrate or ProteinChip®array described above. The sample is preferably a biological fluidsample taken from a subject. Examples of biological fluid samplesinclude blood, serum, plasma, nipple aspirate, urine, tears, saliva etc.In a preferred embodiment, the biological fluid comprises blood serum.The sample can be diluted with a suitable eluant before contacting thesample to the antibody.

After incubating the sample with antibodies, the mixture is washed andthe antibody-marker complex formed can be detected. This can beaccomplished by incubating the washed mixture with a detection reagent.This detection reagent may be, e.g., a second antibody which is labeledwith a detectable label. Exemplary detectable labels include magneticbeads (e.g., DYNABEADS™), fluorescent dyes, radiolabels, enzymes (e.g.,horse radish peroxide, alkaline phosphatase and others commonly used inan ELISA), and colorimetric labels such as colloidal gold or coloredglass or plastic beads. Alternatively, the marker in the sample can bedetected using an indirect assay, wherein, for example, a second,labeled antibody is used to detect bound marker-specific antibody,and/or in a competition or inhibition assay wherein, for example, amonoclonal antibody which binds to a distinct epitope of the marker isincubated simultaneously with the mixture.

Methods for measuring the amount of, or presence of, antibody-markercomplex include, for example, detection of fluorescence, luminescence,chemiluminescence, absorbance, reflectance, transmittance, birefringenceor refractive index (e.g., surface plasmon resonance, ellipsometry, aresonant mirror method, a grating coupler waveguide method orinterferometry). Optical methods include microscopy (both confocal andnon-confocal), imaging methods and non-imaging methods. Electrochemicalmethods include voltametry and amperometry methods. Radio frequencymethods include multipolar resonance spectroscopy. Methods forperforming these assays are readily known in the art. Useful assaysinclude, for example, an enzyme immune assay (EIA) such as enzyme-linkedimmunosorbent assay (ELISA), a radioimmune assay (RIA), a Western blotassay, or a slot blot assay. These methods are also described in, e.g.,Methods in Cell Biology: Antibodies in Cell Biology, volume 37 (Asai,ed. 1993); Basic and Clinical Immunology (Stites & Ten, eds., 7th ed.1991); and Harlow & Lane, supra.

Throughout the assays, incubation and/or washing steps may be requiredafter each combination of reagents. Incubation steps can vary from about5 seconds to several hours, preferably from about 5 minutes to about 24hours. However, the incubation time will depend upon the assay format,marker, volume of solution, concentrations and the like. Usually theassays will be carried out at ambient temperature, although they can beconducted over a range of temperatures, such as 10° C. to 40° C.

Immunoassays can be used to determine presence or absence of a marker ina sample as well as the quantity of a marker in a sample. The amount ofan antibody-marker complex can be determined by comparing to a standard.A standard can be, e.g., a known compound or another protein known to bepresent in a sample. As noted above, the test amount of marker need notbe measured in absolute units, as long as the unit of measurement can becompared to a control.

The methods for detecting these markers in a sample have manyapplications. For example, one or more markers can be measured to aidhuman cancer diagnosis or prognosis. In another example, the methods fordetection of the markers can be used to monitor responses in a subjectto cancer treatment. In another example, the methods for detectingmarkers can be used to assay for and to identify compounds that modulateexpression of these markers in vivo or in vitro. In a preferred example,the biomarkers are used to differentiate between the different stages oftumor progression, thus aiding in determining appropriate treatment andextent of metastasis of the tumor.

C) Combinatorial Ligand Library Beads

Another method of measuring the biomarkers includes the use of acombinatorial ligand library synthesized on beads as described in U.S.Ser. No. 11/495,842, filed Jul. 28, 2006 and entitled “Methods forReducing the range in Concentrations of Analyte Species in a Sample”;hereby incorporated by reference in its entirety.

V. Data Analysis

When the sample is measured and data is generated, e.g., by massspectrometry, the data is then analyzed by a computer software program.Generally, the software can comprise code that converts signal from themass spectrometer into computer readable form. The software also caninclude code that applies an algorithm to the analysis of the signal todetermine whether the signal represents a “peak” in the signalcorresponding to a marker of this invention, or other useful markers.The software also can include code that executes an algorithm thatcompares signal from a test sample to a typical signal characteristic of“normal” and human cancer and determines the closeness of fit betweenthe two signals. The software also can include code indicating which thetest sample is closest to, thereby providing a probable diagnosis.

In preferred methods of the present invention, multiple biomarkers aremeasured. The use of multiple biomarkers increases the predictive valueof the test and provides greater utility in diagnosis, toxicology,patient stratification and patient monitoring. The process called“Pattern recognition” detects the patterns formed by multiple biomarkersgreatly improves the sensitivity and specificity of clinical proteomicsfor predictive medicine. Subtle variations in data from clinicalsamples, e.g., obtained using SELDI, indicate that certain patterns ofprotein expression can predict phenotypes such as the presence orabsence of a certain disease, a particular stage of cancer progression,or a positive or adverse response to drug treatments.

Data generation in mass spectrometry begins with the detection of ionsby an ion detector as described above. Ions that strike the detectorgenerate an electric potential that is digitized by a high speedtime-array recording device that digitally captures the analog signal.Ciphergen's ProteinChip® system employs an analog-to-digital converter(ADC) to accomplish this. The ADC integrates detector output atregularly spaced time intervals into time-dependent bins. The timeintervals typically are one to four nanoseconds long. Furthermore, thetime-of-flight spectrum ultimately analyzed typically does not representthe signal from a single pulse of ionizing energy against a sample, butrather the sum of signals from a number of pulses. This reduces noiseand increases dynamic range. This time-of-flight data is then subject todata processing. In Ciphergen's ProteinChip® software, data processingtypically includes TOF-to-M/Z transformation, baseline subtraction, highfrequency noise filtering.

TOF-to-M/Z transformation involves the application of an algorithm thattransforms times-of-flight into mass-to-charge ratio (M/Z). In thisstep, the signals are converted from the time domain to the mass domain.That is, each time-of-flight is converted into mass-to-charge ratio, orM/Z. Calibration can be done internally or externally. In internalcalibration, the sample analyzed contains one or more analytes of knownM/Z. Signal peaks at times-of-flight representing these massed analytesare assigned the known M/Z. Based on these assigned M/Z ratios,parameters are calculated for a mathematical function that convertstimes-of-flight to M/Z. In external calibration, a function thatconverts times-of-flight to M/Z, such as one created by prior internalcalibration, is applied to a time-of-flight spectrum without the use ofinternal calibrants.

Baseline subtraction improves data quantification by eliminatingartificial, reproducible instrument offsets that perturb the spectrum.It involves calculating a spectrum baseline using an algorithm thatincorporates parameters such as peak width, and then subtracting thebaseline from the mass spectrum.

High frequency noise signals are eliminated by the application of asmoothing function. A typical smoothing function applies a movingaverage function to each time-dependent bin. In an improved version, themoving average filter is a variable width digital filter in which thebandwidth of the filter varies as a function of, e.g., peak bandwidth,generally becoming broader with increased time-of-flight. See, e.g., WO00/70648, Nov. 23, 2000 (Gavin et al., “Variable Width Digital Filterfor Time-of-flight Mass Spectrometry”).

Analysis generally involves the identification of peaks in the spectrumthat represent signal from an analyte. Peak selection can, of course, bedone by eye. However, software is available as part of Ciphergen'sProteinChip® software that can automate the detection of peaks. Ingeneral, this software functions by identifying signals having asignal-to-noise ratio above a selected threshold and labeling the massof the peak at the centroid of the peak signal. In one usefulapplication many spectra are compared to identify identical peakspresent in some selected percentage of the mass spectra. One version ofthis software clusters all peaks appearing in the various spectra withina defined mass range, and assigns a mass (M/Z) to all the peaks that arenear the mid-point of the mass (M/Z) cluster.

Peak data from one or more spectra can be subject to further analysisby, for example, creating a spreadsheet in which each row represents aparticular mass spectrum, each column represents a peak in the spectradefined by mass, and each cell includes the intensity of the peak inthat particular spectrum. Various statistical or pattern recognitionapproaches can applied to the data.

In one example, Ciphergen's Biomarker Patterns™ Software is used todetect a pattern in the spectra that are generated. The data isclassified using a pattern recognition process that uses aclassification model. In general, the spectra will represent samplesfrom at least two different groups for which a classification algorithmis sought. For example, the groups can be pathological v.non-pathological (e.g., cancer v. non-cancer), drug responder v. drugnon-responder, toxic response v. non-toxic response, progressor todisease state v. non-progressor to disease state, phenotypic conditionpresent v. phenotypic condition absent.

The spectra that are generated in embodiments of the invention can beclassified using a pattern recognition process that uses aclassification model. In some embodiments, data derived from the spectra(e.g., mass spectra or time-of-flight spectra) that are generated usingsamples such as “known samples” can then be used to “train” aclassification model. A “known sample” is a sample that ispre-classified (e.g., cancer or not cancer). Data derived from thespectra (e.g., mass spectra or time-of-flight spectra) that aregenerated using samples such as “known samples” can then be used to“train” a classification model. A “known sample” is a sample that ispre-classified. The data that are derived from the spectra and are usedto form the classification model can be referred to as a “training dataset”. Once trained, the classification model can recognize patterns indata derived from spectra generated using unknown samples. Theclassification model can then be used to classify the unknown samplesinto classes. This can be useful, for example, in predicting whether ornot a particular biological sample is associated with a certainbiological condition (e.g., diseased vs. non diseased).

The training data set that is used to form the classification model maycomprise raw data or pre-processed data. In some embodiments, raw datacan be obtained directly from time-of-flight spectra or mass spectra,and then may be optionally “pre-processed” in any suitable manner. Forexample, signals above a predetermined signal-to-noise ratio can beselected so that a subset of peaks in a spectrum is selected, ratherthan selecting all peaks in a spectrum. In another example, apredetermined number of peak “clusters” at a common value (e.g., aparticular time-of-flight value or mass-to-charge ratio value) can beused to select peaks. Illustratively, if a peak at a givenmass-to-charge ratio is in less than 50% of the mass spectra in a groupof mass spectra, then the peak at that mass-to-charge ratio can beomitted from the training data set. Pre-processing steps such as thesecan be used to reduce the amount of data that is used to train theclassification model.

Classification models can be formed using any suitable statisticalclassification (or “learning”) method that attempts to segregate bodiesof data into classes based on objective parameters present in the data.Classification methods may be either supervised or unsupervised.Examples of supervised and unsupervised classification processes aredescribed in Jain, “Statistical Pattern Recognition: A Review”, IEEETransactions on Pattern Analysis and Machine Intelligence, Vol. 22, No.1, January 2000, which is herein incorporated by reference in itsentirety.

In supervised classification, training data containing examples of knowncategories are presented to a learning mechanism, which learns one moresets of relationships that define each of the known classes. New datamay then be applied to the learning mechanism, which then classifies thenew data using the learned relationships. Examples of supervisedclassification processes include linear regression processes (e.g.,multiple linear regression (MLR), partial least squares (PLS) regressionand principal components regression (PCR)), binary decision trees (e.g.,recursive partitioning processes such as CART—classification andregression trees), artificial neural networks such as backpropagationnetworks, discriminant analyses (e.g., Bayesian classifier or Fischeranalysis), logistic classifiers, and support vector classifiers (supportvector machines).

A preferred supervised classification method is a recursive partitioningprocess. Recursive partitioning processes use recursive partitioningtrees to classify spectra derived from unknown samples. Further detailsabout recursive partitioning processes are provided in U.S. 2002 0138208A1 (Paulse et al., “Method for analyzing mass spectra,” Sep. 26, 2002.

In other embodiments, the classification models that are created can beformed using unsupervised learning methods. Unsupervised classificationattempts to learn classifications based on similarities in the trainingdata set, without pre classifying the spectra from which the trainingdata set was derived. Unsupervised learning methods include clusteranalyses. A cluster analysis attempts to divide the data into “clusters”or groups that ideally should have members that are very similar to eachother, and very dissimilar to members of other clusters. Similarity isthen measured using some distance metric, which measures the distancebetween data items, and clusters together data items that are closer toeach other. Clustering techniques include the MacQueen's K-meansalgorithm and the Kohonen's Self-Organizing Map algorithm.

Learning algorithms asserted for use in classifying biologicalinformation are described in, for example, WO 01/31580 (Barnhill et al.,“Methods and devices for identifying patterns in biological systems andmethods of use thereof,” May 3, 2001); U.S. 2002/0193950 A1 (Gavin etal., “Method or analyzing mass spectra,” Dec. 19, 2002); U.S.2003/0004402 A1 (Hitt et al., “Process for discriminating betweenbiological states based on hidden patterns from biological data,” Jan.2, 2003); and U.S. Pat. No. 7,113,896 A1 (Zhang and Zhang, “Systems andmethods for processing biological expression data” Mar. 20, 2003).

More specifically, to obtain the biomarkers, the peak intensity data ofsamples from cancer patients and healthy controls were used as a“discovery set.” This data were combined and randomly divided into atraining set and a test set to construct and test multivariatepredictive models.

Generally, the data generated from Section IV above is inputted into adiagnostic algorithm (i.e., classification algorithm as describedabove). The classification algorithm is then generated based on thelearning algorithm. The process involves developing an algorithm thatcan generate the classification algorithm. The methods of the presentinvention generate a more accurate classification algorithm by accessinga number of ovarian cancer and normal samples of a sufficient numberbased on statistical sample calculations. The samples are used as atraining set of data on learning algorithm.

The generation of the classification, i.e., diagnostic, algorithm isdependent upon the assay protocol used to analyze samples and generatethe data obtained in Section IV above. It is imperative that theprotocol for the detection and/or measurement of the markers (e.g., instep IV) must be the same as that used to obtain the data used fordeveloping the classification algorithm. The assay conditions, whichmust be maintained throughout the training and classification systemsinclude chip type and mass spectrometer parameters, as well as generalprotocols for sample preparation and testing. If the protocol for thedetection and/or measurement of the markers (step IV) is changed, thelearning algorithm and classification algorithm must also change.Similarly, if the learning algorithm and classification algorithmchange, then the protocol for the detection and/or measurement ofmarkers (step IV) must also change to be consistent with that used togenerate classification algorithm. Development of a new classificationmodel would require accessing a sufficient number of ovarian cancer andnormal samples, developing a new training set of data based on a newdetection protocol, generating a new classification algorithm using thedata and finally, verifying the classification algorithm with amulti-site study.

The classification models can be formed on and used on any suitabledigital computer. Suitable digital computers include micro, mini, orlarge computers using any standard or specialized operating system suchas a Unix, Windows™ or Linux™ based operating system. The digitalcomputer that is used may be physically separate from the massspectrometer that is used to create the spectra of interest, or it maybe coupled to the mass spectrometer. If it is separate from the massspectrometer, the data must be inputted into the computer by some othermeans, whether manually or automated.

The training data set and the classification models according toembodiments of the invention can be embodied by computer code that isexecuted or used by a digital computer. The computer code can be storedon any suitable computer readable media including optical or magneticdisks, sticks, tapes, etc., and can be written in any suitable computerprogramming language including C, C++, visual basic, etc.

VI. Diagnosis of Subject and Determination of Ovarian Cancer Status

This panel of biomarkers comparing (i) apolipoprotein A1 (ApoA1), (ii)transthyretin (TTR), (iii) inter-alpha (globulin) inhibitor H4 (plasmaKallikrein-sensitive glycoprotein) (ITIH4), (iv) transferrin (TFR) and(v) CA125, or (i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR),(iii) CTAPIII, and (iv) CA125, or respective fragment thereof, is usefulin aiding in the determination of ovarian cancer status. First, theselected biomarkesr are measured in a subject sample using the methodsdescribed herein, e.g., capture on a SELDI biochip followed by detectionby mass spectrometry. Then, the measurements is compared with adiagnostic amount or control that distinguishes an ovarian cancer statusfrom a non-cancer status. The diagnostic amounts will reflect theinformation herein that the particular biomarkers are up-regulated ordown-regulated in a cancer status compared with a non-cancer status. Asis well understood in the art, the particular diagnostic amounts usedcan be adjusted to increase sensitivity or specificity of the diagnosticassay depending on the preference of the diagnostician. The test amountsas compared with the diagnostic amount thus indicates ovarian cancerstatus.

While individual biomarkers are useful diagnostic markers, it has beenfound that the panels of biomarkers described herein provide greaterdiagnostic value than single markers alone for differentiating betweencancer free patients and patients with early stage, e.g., stage I or IIovarian cancer. Specifically, the detection of a plurality of markers ina sample increases the percentage of true positive and true negativediagnoses and would decrease the percentage of false positive or falsenegative diagnoses. Thus, methods of the present invention comprise themeasurement of more than one biomarker. For example, the methods of thepresent invention have an AUC from ROC analysis greater than 0.50, morepreferred methods have an AUC greater than 0.60, more preferred methodshave an AUC greater than 0.70. Especially preferred methods have an AUCgreater than 0.70 and most preferred methods have an AUC greater than0.80.

In some embodiments, the mere presence or absence of a marker detectedwith a detection cutoff, without quantifying the amount of marker, isuseful and can be correlated with a probable diagnosis of ovariancancer. For example, ITIH4 fragment can be more frequently detected inhuman ovarian cancer patients than in normal subjects. Equally, forexample, biomarkers Apo A1 and transthyretin, can be less frequentlydetected in human ovarian cancer patients than in normal subjects. Also,CA125 is elevated in cancerous cells as compared to non-cancerous cells.Thus, a detected presence or absence, respectively, of these markers ina subject being tested indicates that the subject has a higherprobability of having ovarian cancer.

In other embodiments, the measurement of markers can involve quantifyingthe markers to correlate the detection of markers with a probablediagnosis of ovarian cancer. Thus, if the amount of the markers detectedin a subject being tested is different compared to a control amount(i.e., higher or lower than the control, depending on the marker), thenthe subject being tested has a higher probability of having ovariancancer.

The correlation may take into account the amount of the marker ormarkers in the sample compared to a control amount of the marker ormarkers (up or down regulation of the marker or markers) (e.g., innormal subjects in whom human cancer is undetectable). A control can be,e.g., the average or median amount of marker present in comparablesamples of normal subjects in whom human cancer is undetectable. Thecontrol amount is measured under the same or substantially similarexperimental conditions as in measuring the test amount. The correlationmay take into account the presence or absence of the markers in a testsample and the frequency of detection of the same markers in a control.The correlation may take into account both of such factors to facilitatedetermination of ovarian cancer status.

In certain embodiments of the methods of qualifying ovarian cancerstatus, the methods further comprise managing subject treatment based onthe status. As aforesaid, such management describes the actions of thephysician or clinician subsequent to determining ovarian cancer status.For example, if the result of the methods of the present invention isinconclusive or there is reason that confirmation of status isnecessary, the physician may order more tests. Alternatively, if thestatus indicates that surgery is appropriate, the physician may schedulethe patient for surgery. In other instances, the patient may receivechemotherapy or radiation treatments, either in lieu of, or in additionto, surgery. Likewise, if the result is negative, e.g., the statusindicates late stage ovarian cancer or if the status is otherwise acute,no further action may be warranted. Furthermore, if the results showthat treatment has been successful, no further management may benecessary.

The invention also provides for such methods where the biomarkers (orspecific combination of biomarkers) are measured again after subjectmanagement. In these cases, the methods are used to monitor the statusof the cancer, e.g., response to cancer treatment, remission of thedisease or progression of the disease. Because of the ease of use of themethods and the lack of invasiveness of the methods, the methods can berepeated after each treatment the patient receives. This allows thephysician to follow the effectiveness of the course of treatment. If theresults show that the treatment is not effective, the course oftreatment can be altered accordingly. This enables the physician to beflexible in the treatment options.

In another example, the methods for detecting markers can be used toassay for and to identify compounds that modulate expression of thesemarkers in vivo or in vitro.

The methods of the present invention have other applications as well.For example, the markers can be used to screen for compounds thatmodulate the expression of the markers in vitro or in vivo, whichcompounds in turn may be useful in treating or preventing ovarian cancerin patients. In another example, the markers can be used to monitor theresponse to treatments for ovarian cancer. In yet another example, themarkers can be used in heredity studies to determine if the subject isat risk for developing ovarian cancer. For instance, certain markers maybe genetically linked. This can be determined by, e.g., analyzingsamples from a population of ovarian cancer patients whose families havea history of ovarian cancer. The results can then be compared with dataobtained from, e.g., ovarian cancer patients whose families do not havea history of ovarian cancer. The markers that are genetically linked maybe used as a tool to determine if a subject whose family has a historyof ovarian cancer is pre-disposed to having ovarian cancer.

VIII. Kits

In yet another aspect, the present invention provides kits forqualifying ovarian cancer status, wherein the kits can be used tomeasure the markers of the present invention. For example, the kits canbe used to measure a panel of markers described herein, which markersare differentially present in samples of ovarian cancer patient andnormal subjects. The kits of the invention have many applications. Forexample, the kits can be used to differentiate if a subject has ovariancancer or has a negative diagnosis, thus enabling the physician orclinician to diagnose the presence or absence of the cancer. The kitscan also be used to monitor the patient's response to a course oftreatment, enabling the physician to modify the treatment based upon theresults of the test. In another example, the kits can be used toidentify compounds that modulate expression of one or more of themarkers in in vitro or in vivo animal models for ovarian cancer.

The present invention therefore provides kits comprising (a) a capturereagent that binds a panel of biomarkers comprising (i) apolipoproteinA1 (ApoA1), (ii) transthyretin (TTR), (iii) inter-alpha (globulin)inhibitor H4 (plasma Kallikrein-sensitive glycoprotein) (ITIH4), (iv)transferrin (TFR) and (v) CA125, or (i) apolipoprotein A1 (ApoA1), (ii)transthyretin (TTR), (iii) CTAPIII, and (iv) CA125 or fragments thereof;and (b) a container comprising at least one of the biomarkers.

While the capture reagents can be any type of reagent, preferably thereagent is a SELDI probe. In certain kits of the present invention, thecapture reagent comprises an IMAC.

The invention also provides kits comprising (a) a capture reagents thatbind the panel of biomarkers comprising (i) apolipoprotein A1 (ApoA1),(ii) transthyretin (TTR), (iii) inter-alpha (globulin) inhibitor H4(plasma Kallikrein-sensitive glycoprotein) (ITIH4), (iv) transferrin(TFR) and (v) CA125, or (i) apolipoprotein A1 (ApoA1), (ii)transthyretin (TTR), (iii) CTAPIII, and (iv) CA125 and (b) instructionsfor using the capture reagent to measure the biomarker. In certain ofthese kits, the capture reagents comprise antibodies. Furthermore, someof the aforesaid kits further comprise an MS probe to which the capturereagent is attached or is attachable. In some kits, the capture reagentcomprises an IMAC. One preferred embodiment of the present inventionincludes a high-throughput test for early detection of ovarian cancer,which analyzes a patient's sample on the IMAC ProteinChip® array for theanalytes.

Certain kits of the present invention further comprise a wash solution,or eluant, that selectively allows retention of the bound biomarkers tothe capture reagents as compared with other biomarkers after washing.Alternatively, the kit may contain instructions for making a washsolution, wherein the combination of the adsorbent and the wash solutionallows detection of the markers using gas phase ion spectrometry.

Preferably, the kit comprises written instructions for use of the kitfor detection of cancer and the instructions provide for contacting atest sample with the capture reagents and detecting the panel ofbiomarkers retained by the capture reagents. For example, the kit mayhave standard instructions informing a consumer how to wash the capturereagents (e.g., probes) after a sample of blood serum contacts thecapture reagents. In another example, the kit may have instructions forpre-fractionating a sample to reduce complexity of proteins in thesample. In another example, the kit may have instructions for automatingthe fractionation or other processes.

Such kits can be prepared from the materials described above, and theprevious discussion of these materials (e.g., probe substrates, capturereagents, adsorbents, washing solutions, etc.) is fully applicable tothis section and will not be repeated.

In another embodiment, a kit comprises (a) antibodies that specificallybind to the panel of biomarkers; and (b) a detection reagent. Such kitscan be prepared from the materials described above, and the previousdiscussion regarding the materials (e.g., antibodies, detectionreagents, immobilized supports, etc.) is fully applicable to thissection and will not be repeated. Optionally, the kit may furthercomprise pre-fractionation spin columns. In some embodiments, the kitmay further comprise instructions for suitable operation parameters inthe form of a label or a separate insert.

Optionally, the kit may further comprise a standard or controlinformation so that the test sample can be compared with the controlinformation standard to determine if the test amount of a markerdetected in a sample is a diagnostic amount consistent with a diagnosisof ovarian cancer.

The invention also provides an article manufacture comprising at leastone capture reagent bound to at least two biomarkers selected from (i)apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii) inter-alpha(globulin) inhibitor H4 (plasma Kallikrein-sensitive glycoprotein)(ITIH4), (iv) transferrin (TFR) and (v) CA125, or (i) apolipoprotein A1(ApoA1), (ii) transthyretin (TTR), (iii) CTAPIII, and (iv) CA125.Examples of articles of manufacture of the present invention include,but are not limited to, ProteinChip® Arrays, probes, microtitre plates,beads, test tubes, microtubes, and any other solid phase onto which acapture reagent can be incorporated.

The present invention also provides a system comprising a plurality ofcapture reagents each of which has bound to it a different biomarkercomprising (i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR),(iii) inter-alpha (globulin) inhibitor H4 (plasma Kallikrein-sensitiveglycoprotein) (ITIH4), (iv) transferrin (TFR) and (v) CA125, or (i)apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii) CTAPIII, and(iv) CA125 or respective fragment thereof. Examples of other systemsinclude those in which the capture reagents are test tubes containing anantibody for each of the biomarkers, either separately, or in groups.One of ordinary skill in the art would readily be able to manufactureother such articles in accordance with the teachings described herein.

The following examples are offered by way of illustration, not by way oflimitation. While specific examples have been provided, the abovedescription is illustrative and not restrictive. Any one or more of thefeatures of the previously described embodiments can be combined in anymanner with one or more features of any other embodiments in the presentinvention. Furthermore, many variations of the invention will becomeapparent to those skilled in the art upon review of the specification.The scope of the invention should, therefore, be determined not withreference to the above description, but instead should be determinedwith reference to the appended claims along with their full scope ofequivalents.

All publications and patent documents cited in this application areincorporated by reference in their entirety for all purposes to the sameextent as if each individual publication or patent document were soindividually denoted. By their citation of various references in thisdocument, Applicants do not admit any particular reference is “priorart” to their invention.

Examples Proteomic Markers Enhance the Sensitivity of Ca 125 forDetection of Stage I Epithelial Ovarian Cancer

Given the prevalence of ovarian cancer, a successful strategy for thedetection of early stage disease will require both a high sensitivity(>90%) and specificity (99.6%). The requisite specificity might beattained through a two stage strategy that utilizes an abnormal valuefor serum markers (s) to prompt the performance of transvaginalsonography (TVS). To perform TVS in a maximum of 2% of individualsscreened, a specificity of 98% would be required. Greater sensitivity at98% specificity than obtained with CA 125 alone might be achieved withthe addition of proteomic biomarkers. The aim of this study was to testa panel of proteomic serum biomarkers for their ability to enhance CA125in distinguishing individuals with early stage ovarian cancer fromhealthy subjects and from women with benign pelvic masses.

Materials and Methods:

Samples

We obtained pre-operative serum samples from the MDACC Ovarian CancerTumor Bank consisting of 92 patients with epithelial ovarian cancer (22stage I, 19 stage II, 41 stage III and 10 stage IV), 40 with benigndisease and 99 healthy individuals.

Sample Measurements

Chromatographic SELDI-TOF-MS protocols have been developed to generatequantitative measurements. Standard curves were generated using knownquantities of calibrants. Samples were processed in triplicate andrandomized in 96 well bioprocessors using a Biomek2000 robot. Sampleswere processed using a Tecan Aquarius™ robot, and protocols performedusing different ProteinChip® Arrays. Samples were diluted in respectivebuffers to assay. Transthyretin (TT), apolipoprotein, A1 (ApoA1),connective-tissue activating protein III (CTAPIII), inter-alpha trypsininhibitor IV (internal fragment) (ITIH4), hepicidin (HepC), beta-2microglobulin (B2M) and transferrin. In the TT, ApoA1 and CTAPIIIassays, E. coli lysate was mixed with the binding buffers. TT wasassayed on Q10 arrays, ApoA1 was assayed on H50 arrays, CTAPIII and B2Mwere assayed together on IMAC50-Cu arrays, and ITIH4, HepC andtransferrin were assayed together on IMAC50-Cu arrays. Arrays wereanalyzed on a PCS4000 mass spectrometer.

Analysis

Peak intensity data were baseline subtracted and normalized using eithertotal ion current factors or peak ratio factors. TIC normalized valueswere used for Hepcidin, ITIH4, Transferrin, and B2M. Ratio to average Ecoli peak intensity were used for CTAP3, TT ratio, and ApoA1. Modelswere fit using logistic regression, followed by a stepwise optimizationof the factors retained in the models determined by optimizing theAkaike Information Criterion.

TABLE 1 Description of patient samples Median Age Median CA125 SampleType Number (range) (range) Stage I 22 49 (20-87) 56 (8.3-1273) Stage II19 61 (41-88) 190 (7-2000) Stage III, IV 51 57 (31-85) 420 (6.5-26,000)Benign 40 45 (26-80) 17 (2.1-189) Normal 99 62 (51-77) 11 (3.9-73)

Results:

Two isoforms of transthyretin were combined to calculate theconcentration of this marker. In the analysis of this patientpopulation, it was found that subsets of the measured markers weresufficient for building the classification models.

Markers Mass Transthyretin (cysteinylated form) 13880.2 Transthyretin(un-modified form) 13841.0 Apolipoprotein A1 28079.6 ITIH4 fragment3273.7 Transferrin 79908 Hepcidin-25 2790.4 CTAPIII 9288.7 Beta2-microglobin 11731.2

Marker subsets used in Model development

-   -   Normal versus Stage I: ApoA1, TT, ITIH4, Transferrin, CA 125    -   Normal versus Stages I, II: ApoA1, TT, CTAPIII, CA125    -   Normal versus Stages I-IV: ApoA1, TT, CTAPIII, CA125    -   Normal and Benign versus any cancer grouping: ApoA1, TT,        CTAPIII, CA125

FIG. 2 depicts ROC curve plots for models trained on six subsets ofdata. The black dot represents CA125 performance alone. Performance ofthe models, however, was always assessed by comparing normal controls toearly stage (I or II) ovarian cancer. Thus, specificity refers to theability of a model to correctly classify normal samples, and sensitivityrefers to the ability of model to correctly classify early stage cancer.

TABLE 3 Sensitivities of Models at 98% Specificity A) CA125 Alone B)Proteomic Markers Alone Comparison Sensitivity Comparison Normal versusstage I 0.68 Normal versus stage I Normal versus stage I and II 0.71Normal versus stage I and II Normal versus stage I-IV 0.76 Normal versusstage I-IV C) Proteomic Markers + CA 125 Comparison Sensitivity Normalversus stage I 0.80 Normal versus stage I and II 0.88 Normal versusstage I-IV 0.85

Conclusion: A panel of proteomic biomarkers in combination with CA125improves detection of early stage ovarian cancer.

The present invention has been described in detail, including thepreferred embodiments thereof. However, it will be appreciated thatthose skilled in the art, upon consideration of the present disclosure,may make modifications and/or improvements of this invention and stillbe within the scope and spirit of this invention as set forth in thefollowing claims.

All publications and patent documents cited in this application areincorporated by reference in their entirety for all purposes to the sameextent as if each individual publication or patent document were soindividually denoted. By their citation of various references in thisdocument, Applicants do not admit any particular reference is “priorart” to their invention.

1. A method of determining an ovarian cancer patient's ovarian cancerstatus comprising: (a) determining the concentration or expressionlevels or peak intensity values of a combination of biomarkers in asample from the subject, wherein the biomarkers comprise: (i)apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii) inter-alpha(globulin) inhibitor H4 (plasma Kallikrein-sensitive glycoprotein)(ITIH4), (iv) transferrin (TFR) and (v) CA125, or (i) apolipoprotein A1(ApoA1), (ii) transthyretin (TTR), (iii) CTAPIII, and (iv) CA125; and(b) correlating the measurements with ovarian cancer status.
 2. A methodof determining if a patient has early stage ovarian cancer comprising:(a) determining the concentration/expression levels/peak intensityvalues of a panel seven biomarkers in a sample from the subject, whereinthe biomarkers (approximate m/z location) are (i) apolipoprotein A1(ApoA1), (ii) transthyretin (TTR), (iii) inter-alpha (globulin)inhibitor H4 (plasma Kallikrein-sensitive glycoprotein) (ITIH4), (iv)transferrin (TFR) and (v) CA125 or (i) apolipoprotein A1 (ApoA1), (ii)transthyretin (TTR), (iii) CTAPIII, and (iv) CA125; and (b) correlatingthe measurement with ovarian cancer status
 3. The method of claim 2,wherein the early stage ovarian cancer is stage I ovarian cancer.
 4. Themethod of claim 2, wherein the early stage ovarian cancer is stage IIovarian cancer
 5. The method of any one of claim 1 further comprising:(c) managing subject treatment based on the status.
 6. The method ofclaim 5, wherein managing subject treatment is selected from orderingmore tests, performing surgery, and taking no further action.
 7. Themethod of claim 8 further comprising: (d) determining the m/z values forthe panel of biomarkers after subject management.
 8. A method ofqualifying ovarian cancer status in a subject comprising: (a) providinga subject sample of blood or a blood derivative; (b) fractionatingproteins in the sample on an anion exchange resin and collectingfractions that contain (i) apolipoprotein A1 (ApoA1), (ii) transthyretin(TTR), (iii) inter-alpha (globulin) inhibitor H4 (plasmaKallikrein-sensitive glycoprotein) (ITIH4), (iv) transferrin (TFR) and(v) CA125 or (i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR),(iii) CTAPIII, and (iv) CA125; and (c) capturing (i) apolipoprotein A1(ApoA1), (ii) transthyretin (TTR), (iii) inter-alpha (globulin)inhibitor H4 (plasma Kallikrein-sensitive glycoprotein) (ITIH4), (iv)transferrin (TFR) and (v) CA125 or (i) apolipoprotein A1 (ApoA1), (ii)transthyretin (TTR), (iii) CTAPIII, and (iv) CA125 from the fractions ona surface of a substrate comprising capture reagents that bind theprotein biomarkers.
 9. The method of claim 8 wherein the substrate is aSELDI probe comprising an IMAC copper surface and wherein the proteinbiomarkers are detected by SELDI.
 10. The method of claim 9 wherein thesubstrate is a SELDI probe comprising biospecific affinity reagents thatbind (i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii)inter-alpha (globulin) inhibitor H4 (plasma Kallikrein-sensitiveglycoprotein) (ITIH4), (iv) transferrin (TFR) and (v) CA125 or (i)apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii) CTAPIII, and(iv) CA125 and wherein the protein biomarkers are detected by SELDI. 11.The method of claim 8 wherein the substrate is a microtiter platecomprising biospecific affinity reagents that bind (i) apolipoprotein A1(ApoA1), (ii) transthyretin (TTR), (iii) inter-alpha (globulin)inhibitor H4 (plasma Kallikrein-sensitive glycoprotein) (ITIH4), (iv)transferrin (TFR) and (v) CA125 or (i) apolipoprotein A1 (ApoA1), (ii)transthyretin (TTR), (iii) CTAPIII, and (iv) CA125 and the proteinbiomarkers are detected by immunoassay.
 12. The method of claim 11,wherein measuring is selected from detecting the presence or absence ofthe biomarkers(s), quantifying the amount of marker(s), and qualifyingthe type of biomarker.
 13. The method of claim 1 wherein the panel ofbiomarkers are measured using a biochip array.
 14. The method of claim13 wherein the biochip array is a protein chip array.
 15. The method ofclaim 14 wherein the biochip array is a nucleic acid array.
 16. Themethod of claim 15 wherein the panel of biomarkers are immobilized onthe biochip array.
 17. The method of claim 1 wherein the panel ofprotein biomarkers are measured by SELDI.
 18. The method of claim 1wherein the panel of protein biomarkers are measured by immunoassay. 19.The method of claim 1 wherein the correlating is performed by a softwareclassification algorithm.
 20. The method of claim 1 wherein the sampleis selected from blood, serum and plasma.
 21. A kit comprising: (a) acapture reagent that binds a panel of biomarkers comprising (i)apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii) inter-alpha(globulin) inhibitor H4 (plasma Kallikrein-sensitive glycoprotein)(ITIH4), (iv) transferrin (TFR) and (v) CA125, and (b) a containercomprising at the panel of biomarkers.
 22. A kit comprising: (a) acapture reagent that binds a panel of biomarkers comprising (i)apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii) CTAPIII, and(iv) CA125; and (b) a container comprising at the panel of biomarkers.23. The kit of claim 21 wherein the capture reagents binds a pluralityof the biomarkers.
 24. The kit of claim 21 wherein the capture reagentsare SELDI probes.
 25. The kit of claim 21 wherein the capture reagentsare an immobilized metal chelate.
 26. The kit of claim 21 furthercomprising a wash solution that selectively allows retention of thebound biomarker to the capture reagent as compared with other biomarkersafter washing.
 27. A system comprising: (a) a plurality of capturereagents each of which has bound to it a different biomarker comprising(i) apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii)inter-alpha (globulin) inhibitor H4 (plasma Kallikrein-sensitiveglycoprotein) (ITIH4), (iv) transferrin (TFR) and (v) CA125 or (i)apolipoprotein A1 (ApoA1), (ii) transthyretin (TTR), (iii) CTAPIII, and(iv) CA125.